Overview

Brought to you by YData

Dataset statistics

Number of variables66
Number of observations4787
Missing cells123708
Missing cells (%)39.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory528.0 B

Variable types

Numeric15
Text25
Categorical14
DateTime4
Unsupported8

Alerts

_embedded.show.averageRuntime is highly overall correlated with _embedded.show.network.country.code and 6 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly overall correlated with _embedded.show.externals.tvrage and 9 other fieldsHigh correlation
_embedded.show.externals.tvrage is highly overall correlated with _embedded.show.externals.thetvdb and 13 other fieldsHigh correlation
_embedded.show.id is highly overall correlated with _embedded.show.externals.thetvdb and 6 other fieldsHigh correlation
_embedded.show.language is highly overall correlated with _embedded.show.externals.tvrage and 10 other fieldsHigh correlation
_embedded.show.network.country.code is highly overall correlated with _embedded.show.averageRuntime and 23 other fieldsHigh correlation
_embedded.show.network.country.name is highly overall correlated with _embedded.show.averageRuntime and 23 other fieldsHigh correlation
_embedded.show.network.country.timezone is highly overall correlated with _embedded.show.averageRuntime and 23 other fieldsHigh correlation
_embedded.show.network.id is highly overall correlated with _embedded.show.externals.tvrage and 13 other fieldsHigh correlation
_embedded.show.network.name is highly overall correlated with _embedded.show.averageRuntime and 25 other fieldsHigh correlation
_embedded.show.network.officialSite is highly overall correlated with _embedded.show.averageRuntime and 25 other fieldsHigh correlation
_embedded.show.rating.average is highly overall correlated with _embedded.show.network.country.code and 5 other fieldsHigh correlation
_embedded.show.runtime is highly overall correlated with _embedded.show.averageRuntime and 8 other fieldsHigh correlation
_embedded.show.schedule.time is highly overall correlated with _embedded.show.externals.thetvdb and 9 other fieldsHigh correlation
_embedded.show.status is highly overall correlated with _embedded.show.externals.tvrage and 10 other fieldsHigh correlation
_embedded.show.type is highly overall correlated with _embedded.show.externals.tvrage and 4 other fieldsHigh correlation
_embedded.show.updated is highly overall correlated with _embedded.show.network.country.code and 4 other fieldsHigh correlation
_embedded.show.webChannel.country.code is highly overall correlated with _embedded.show.externals.tvrage and 12 other fieldsHigh correlation
_embedded.show.webChannel.country.name is highly overall correlated with _embedded.show.externals.tvrage and 12 other fieldsHigh correlation
_embedded.show.webChannel.country.timezone is highly overall correlated with _embedded.show.externals.tvrage and 12 other fieldsHigh correlation
_embedded.show.webChannel.id is highly overall correlated with _embedded.show.network.country.code and 7 other fieldsHigh correlation
_embedded.show.weight is highly overall correlated with _embedded.show.externals.thetvdb and 6 other fieldsHigh correlation
id is highly overall correlated with _embedded.show.network.name and 1 other fieldsHigh correlation
number is highly overall correlated with _embedded.show.network.name and 3 other fieldsHigh correlation
rating.average is highly overall correlated with _embedded.show.network.country.code and 3 other fieldsHigh correlation
runtime is highly overall correlated with _embedded.show.averageRuntime and 7 other fieldsHigh correlation
season is highly overall correlated with _embedded.show.externals.thetvdb and 11 other fieldsHigh correlation
type is highly overall correlated with _embedded.show.network.country.code and 6 other fieldsHigh correlation
type is highly imbalanced (96.3%) Imbalance
_embedded.show.schedule.time is highly imbalanced (50.6%) Imbalance
airtime has 2460 (51.4%) missing values Missing
runtime has 453 (9.5%) missing values Missing
summary has 3312 (69.2%) missing values Missing
rating.average has 4448 (92.9%) missing values Missing
image.medium has 3561 (74.4%) missing values Missing
image.original has 3561 (74.4%) missing values Missing
_embedded.show.language has 330 (6.9%) missing values Missing
_embedded.show.runtime has 3582 (74.8%) missing values Missing
_embedded.show.averageRuntime has 309 (6.5%) missing values Missing
_embedded.show.ended has 3066 (64.0%) missing values Missing
_embedded.show.officialSite has 473 (9.9%) missing values Missing
_embedded.show.rating.average has 4048 (84.6%) missing values Missing
_embedded.show.network has 4787 (100.0%) missing values Missing
_embedded.show.webChannel.id has 112 (2.3%) missing values Missing
_embedded.show.webChannel.name has 112 (2.3%) missing values Missing
_embedded.show.webChannel.country.name has 1602 (33.5%) missing values Missing
_embedded.show.webChannel.country.code has 1602 (33.5%) missing values Missing
_embedded.show.webChannel.country.timezone has 1602 (33.5%) missing values Missing
_embedded.show.webChannel.officialSite has 1334 (27.9%) missing values Missing
_embedded.show.dvdCountry has 4787 (100.0%) missing values Missing
_embedded.show.externals.tvrage has 4609 (96.3%) missing values Missing
_embedded.show.externals.thetvdb has 1504 (31.4%) missing values Missing
_embedded.show.externals.imdb has 2635 (55.0%) missing values Missing
_embedded.show.image.medium has 253 (5.3%) missing values Missing
_embedded.show.image.original has 253 (5.3%) missing values Missing
_embedded.show.summary has 795 (16.6%) missing values Missing
image has 4787 (100.0%) missing values Missing
_embedded.show.image has 4787 (100.0%) missing values Missing
_embedded.show._links.nextepisode.href has 4304 (89.9%) missing values Missing
_embedded.show._links.nextepisode.name has 4304 (89.9%) missing values Missing
_embedded.show.network.id has 4271 (89.2%) missing values Missing
_embedded.show.network.name has 4271 (89.2%) missing values Missing
_embedded.show.network.country.name has 4271 (89.2%) missing values Missing
_embedded.show.network.country.code has 4271 (89.2%) missing values Missing
_embedded.show.network.country.timezone has 4271 (89.2%) missing values Missing
_embedded.show.network.officialSite has 4629 (96.7%) missing values Missing
_embedded.show.webChannel has 4787 (100.0%) missing values Missing
_embedded.show.webChannel.country has 4787 (100.0%) missing values Missing
_embedded.show.dvdCountry.name has 4783 (99.9%) missing values Missing
_embedded.show.dvdCountry.code has 4783 (99.9%) missing values Missing
_embedded.show.dvdCountry.timezone has 4783 (99.9%) missing values Missing
id has unique values Unique
url has unique values Unique
_links.self.href has unique values Unique
_embedded.show.genres is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.schedule.days is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.network is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.dvdCountry is an unsupported type, check if it needs cleaning or further analysis Unsupported
image is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.image is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.webChannel is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.webChannel.country is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.weight has 134 (2.8%) zeros Zeros

Reproduction

Analysis started2024-11-24 21:51:01.256384
Analysis finished2024-11-24 21:51:11.599635
Duration10.34 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct4787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2770833.6
Minimum2391730
Maximum3064530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:11.631498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2391730
5-th percentile2693730.3
Q12732543.5
median2744507
Q32781530.5
95-th percentile2953070.7
Maximum3064530
Range672800
Interquartile range (IQR)48987

Descriptive statistics

Standard deviation75194.1
Coefficient of variation (CV)0.027137717
Kurtosis3.023331
Mean2770833.6
Median Absolute Deviation (MAD)14686
Skewness1.6949786
Sum1.3263981 × 1010
Variance5.6541526 × 109
MonotonicityNot monotonic
2024-11-24T16:51:11.682896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2737127 1
 
< 0.1%
2749966 1
 
< 0.1%
2744345 1
 
< 0.1%
2758764 1
 
< 0.1%
2758763 1
 
< 0.1%
2758762 1
 
< 0.1%
2758761 1
 
< 0.1%
2737252 1
 
< 0.1%
2758818 1
 
< 0.1%
2845127 1
 
< 0.1%
Other values (4777) 4777
99.8%
ValueCountFrequency (%)
2391730 1
< 0.1%
2494160 1
< 0.1%
2580338 1
< 0.1%
2580339 1
< 0.1%
2610881 1
< 0.1%
2610882 1
< 0.1%
2625941 1
< 0.1%
2633274 1
< 0.1%
2633275 1
< 0.1%
2633276 1
< 0.1%
ValueCountFrequency (%)
3064530 1
< 0.1%
3064529 1
< 0.1%
3064467 1
< 0.1%
3064466 1
< 0.1%
3064465 1
< 0.1%
3064464 1
< 0.1%
3064463 1
< 0.1%
3064462 1
< 0.1%
3064461 1
< 0.1%
3064460 1
< 0.1%

url
Text

Unique 

Distinct4787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:11.852408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length192
Median length147
Mean length79.138082
Min length53

Characters and Unicode

Total characters378834
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4787 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2737127/restoran-po-ponatiam-3x12-seria-34
2nd rowhttps://www.tvmaze.com/episodes/2688265/sny-alisy-1x08-seria-08
3rd rowhttps://www.tvmaze.com/episodes/2749976/smotrite-sami-s-ok-1x116-seria-116
4th rowhttps://www.tvmaze.com/episodes/2752362/bolsoe-sou-s07-special-bolsoe-sou-7-sezon-rassirennaa-versia
5th rowhttps://www.tvmaze.com/episodes/2745754/zolotoe-dno-1x01-seria-1
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2737127/restoran-po-ponatiam-3x12-seria-34 1
 
< 0.1%
https://www.tvmaze.com/episodes/2749976/smotrite-sami-s-ok-1x116-seria-116 1
 
< 0.1%
https://www.tvmaze.com/episodes/2745754/zolotoe-dno-1x01-seria-1 1
 
< 0.1%
https://www.tvmaze.com/episodes/2745755/zolotoe-dno-1x02-seria-2 1
 
< 0.1%
https://www.tvmaze.com/episodes/2745756/zolotoe-dno-1x03-seria-3 1
 
< 0.1%
https://www.tvmaze.com/episodes/2745757/zolotoe-dno-1x04-seria-4 1
 
< 0.1%
https://www.tvmaze.com/episodes/2745758/zolotoe-dno-1x05-seria-5 1
 
< 0.1%
https://www.tvmaze.com/episodes/2745759/zolotoe-dno-1x06-seria-6 1
 
< 0.1%
https://www.tvmaze.com/episodes/2745760/zolotoe-dno-1x07-seria-7 1
 
< 0.1%
https://www.tvmaze.com/episodes/2745761/zolotoe-dno-1x08-seria-8 1
 
< 0.1%
Other values (4777) 4777
99.8%
2024-11-24T16:51:12.081205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31973
 
8.4%
- 29908
 
7.9%
s 24331
 
6.4%
/ 23935
 
6.3%
t 21812
 
5.8%
o 20292
 
5.4%
w 16398
 
4.3%
a 15144
 
4.0%
i 15047
 
4.0%
p 14299
 
3.8%
Other values (30) 165695
43.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 378834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 31973
 
8.4%
- 29908
 
7.9%
s 24331
 
6.4%
/ 23935
 
6.3%
t 21812
 
5.8%
o 20292
 
5.4%
w 16398
 
4.3%
a 15144
 
4.0%
i 15047
 
4.0%
p 14299
 
3.8%
Other values (30) 165695
43.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 378834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 31973
 
8.4%
- 29908
 
7.9%
s 24331
 
6.4%
/ 23935
 
6.3%
t 21812
 
5.8%
o 20292
 
5.4%
w 16398
 
4.3%
a 15144
 
4.0%
i 15047
 
4.0%
p 14299
 
3.8%
Other values (30) 165695
43.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 378834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 31973
 
8.4%
- 29908
 
7.9%
s 24331
 
6.4%
/ 23935
 
6.3%
t 21812
 
5.8%
o 20292
 
5.4%
w 16398
 
4.3%
a 15144
 
4.0%
i 15047
 
4.0%
p 14299
 
3.8%
Other values (30) 165695
43.7%

name
Text

Distinct2379
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:12.247182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length129
Median length121
Mean length15.007729
Min length2

Characters and Unicode

Total characters71842
Distinct characters437
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2190 ?
Unique (%)45.7%

Sample

1st rowСерия 34
2nd rowСерия 08
3rd rowСерия 116
4th rowБольшое шоу 7 сезон. Расширенная версия
5th rowСерия 1
ValueCountFrequency (%)
episode 2464
 
18.2%
the 382
 
2.8%
1 193
 
1.4%
2 192
 
1.4%
серия 180
 
1.3%
3 157
 
1.2%
4 148
 
1.1%
141
 
1.0%
5 135
 
1.0%
6 129
 
1.0%
Other values (4376) 9429
69.6%
2024-11-24T16:51:12.473185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8771
 
12.2%
e 5902
 
8.2%
o 4613
 
6.4%
i 4434
 
6.2%
s 4117
 
5.7%
d 3426
 
4.8%
p 2941
 
4.1%
E 2789
 
3.9%
a 2596
 
3.6%
n 2148
 
3.0%
Other values (427) 30105
41.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71842
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8771
 
12.2%
e 5902
 
8.2%
o 4613
 
6.4%
i 4434
 
6.2%
s 4117
 
5.7%
d 3426
 
4.8%
p 2941
 
4.1%
E 2789
 
3.9%
a 2596
 
3.6%
n 2148
 
3.0%
Other values (427) 30105
41.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71842
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8771
 
12.2%
e 5902
 
8.2%
o 4613
 
6.4%
i 4434
 
6.2%
s 4117
 
5.7%
d 3426
 
4.8%
p 2941
 
4.1%
E 2789
 
3.9%
a 2596
 
3.6%
n 2148
 
3.0%
Other values (427) 30105
41.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71842
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8771
 
12.2%
e 5902
 
8.2%
o 4613
 
6.4%
i 4434
 
6.2%
s 4117
 
5.7%
d 3426
 
4.8%
p 2941
 
4.1%
E 2789
 
3.9%
a 2596
 
3.6%
n 2148
 
3.0%
Other values (427) 30105
41.9%

season
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.95049
Minimum1
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:12.533811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q36
95-th percentile2024
Maximum2024
Range2023
Interquartile range (IQR)5

Descriptive statistics

Standard deviation713.22949
Coefficient of variation (CV)2.3937853
Kurtosis2.0317001
Mean297.95049
Median Absolute Deviation (MAD)0
Skewness2.0075815
Sum1426289
Variance508696.31
MonotonicityNot monotonic
2024-11-24T16:51:12.577771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 2519
52.6%
2024 694
 
14.5%
2 559
 
11.7%
3 259
 
5.4%
5 120
 
2.5%
4 114
 
2.4%
6 73
 
1.5%
8 66
 
1.4%
25 36
 
0.8%
11 33
 
0.7%
Other values (24) 314
 
6.6%
ValueCountFrequency (%)
1 2519
52.6%
2 559
 
11.7%
3 259
 
5.4%
4 114
 
2.4%
5 120
 
2.5%
6 73
 
1.5%
7 25
 
0.5%
8 66
 
1.4%
9 27
 
0.6%
10 30
 
0.6%
ValueCountFrequency (%)
2024 694
14.5%
2023 4
 
0.1%
54 4
 
0.1%
50 3
 
0.1%
41 8
 
0.2%
39 19
 
0.4%
34 4
 
0.1%
31 5
 
0.1%
30 20
 
0.4%
27 6
 
0.1%

number
Real number (ℝ)

High correlation 

Distinct183
Distinct (%)3.8%
Missing29
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean19.053594
Minimum1
Maximum959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:12.624607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q318
95-th percentile66.15
Maximum959
Range958
Interquartile range (IQR)14

Descriptive statistics

Standard deviation47.686153
Coefficient of variation (CV)2.502738
Kurtosis175.83037
Mean19.053594
Median Absolute Deviation (MAD)6
Skewness11.079164
Sum90657
Variance2273.9692
MonotonicityNot monotonic
2024-11-24T16:51:12.673008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 400
 
8.4%
2 374
 
7.8%
3 353
 
7.4%
4 314
 
6.6%
5 277
 
5.8%
6 258
 
5.4%
7 216
 
4.5%
8 205
 
4.3%
9 159
 
3.3%
10 148
 
3.1%
Other values (173) 2054
42.9%
ValueCountFrequency (%)
1 400
8.4%
2 374
7.8%
3 353
7.4%
4 314
6.6%
5 277
5.8%
6 258
5.4%
7 216
4.5%
8 205
4.3%
9 159
 
3.3%
10 148
 
3.1%
ValueCountFrequency (%)
959 1
< 0.1%
958 1
< 0.1%
957 1
< 0.1%
956 1
< 0.1%
955 1
< 0.1%
407 1
< 0.1%
406 1
< 0.1%
405 1
< 0.1%
404 1
< 0.1%
403 1
< 0.1%

type
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
regular
4758 
significant_special
 
18
insignificant_special
 
11

Length

Max length21
Median length7
Mean length7.0772927
Min length7

Characters and Unicode

Total characters33879
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowsignificant_special
5th rowregular

Common Values

ValueCountFrequency (%)
regular 4758
99.4%
significant_special 18
 
0.4%
insignificant_special 11
 
0.2%

Length

2024-11-24T16:51:12.717602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-24T16:51:12.755324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
regular 4758
99.4%
significant_special 18
 
0.4%
insignificant_special 11
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 9516
28.1%
a 4816
14.2%
e 4787
14.1%
g 4787
14.1%
l 4787
14.1%
u 4758
14.0%
i 127
 
0.4%
n 69
 
0.2%
s 58
 
0.2%
c 58
 
0.2%
Other values (4) 116
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33879
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 9516
28.1%
a 4816
14.2%
e 4787
14.1%
g 4787
14.1%
l 4787
14.1%
u 4758
14.0%
i 127
 
0.4%
n 69
 
0.2%
s 58
 
0.2%
c 58
 
0.2%
Other values (4) 116
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33879
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 9516
28.1%
a 4816
14.2%
e 4787
14.1%
g 4787
14.1%
l 4787
14.1%
u 4758
14.0%
i 127
 
0.4%
n 69
 
0.2%
s 58
 
0.2%
c 58
 
0.2%
Other values (4) 116
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33879
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 9516
28.1%
a 4816
14.2%
e 4787
14.1%
g 4787
14.1%
l 4787
14.1%
u 4758
14.0%
i 127
 
0.4%
n 69
 
0.2%
s 58
 
0.2%
c 58
 
0.2%
Other values (4) 116
 
0.3%

airdate
Categorical

Distinct31
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-01-26
 
302
2024-01-19
 
268
2024-01-11
 
232
2024-01-18
 
217
2024-01-25
 
214
Other values (26)
3554 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters47870
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-18
2nd row2024-01-18
3rd row2024-01-18
4th row2024-01-18
5th row2024-01-18

Common Values

ValueCountFrequency (%)
2024-01-26 302
 
6.3%
2024-01-19 268
 
5.6%
2024-01-11 232
 
4.8%
2024-01-18 217
 
4.5%
2024-01-25 214
 
4.5%
2024-01-08 211
 
4.4%
2024-01-22 200
 
4.2%
2024-01-01 189
 
3.9%
2024-01-24 179
 
3.7%
2024-01-12 175
 
3.7%
Other values (21) 2600
54.3%

Length

2024-11-24T16:51:12.790172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-01-26 302
 
6.3%
2024-01-19 268
 
5.6%
2024-01-11 232
 
4.8%
2024-01-18 217
 
4.5%
2024-01-25 214
 
4.5%
2024-01-08 211
 
4.4%
2024-01-22 200
 
4.2%
2024-01-01 189
 
3.9%
2024-01-24 179
 
3.7%
2024-01-12 175
 
3.7%
Other values (21) 2600
54.3%

Most occurring characters

ValueCountFrequency (%)
2 11647
24.3%
0 11235
23.5%
- 9574
20.0%
1 7132
14.9%
4 5220
10.9%
3 641
 
1.3%
9 556
 
1.2%
5 530
 
1.1%
8 517
 
1.1%
6 489
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 11647
24.3%
0 11235
23.5%
- 9574
20.0%
1 7132
14.9%
4 5220
10.9%
3 641
 
1.3%
9 556
 
1.2%
5 530
 
1.1%
8 517
 
1.1%
6 489
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 11647
24.3%
0 11235
23.5%
- 9574
20.0%
1 7132
14.9%
4 5220
10.9%
3 641
 
1.3%
9 556
 
1.2%
5 530
 
1.1%
8 517
 
1.1%
6 489
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 11647
24.3%
0 11235
23.5%
- 9574
20.0%
1 7132
14.9%
4 5220
10.9%
3 641
 
1.3%
9 556
 
1.2%
5 530
 
1.1%
8 517
 
1.1%
6 489
 
1.0%

airtime
Date

Missing 

Distinct65
Distinct (%)2.8%
Missing2460
Missing (%)51.4%
Memory size37.5 KiB
Minimum2024-11-24 00:00:00
Maximum2024-11-24 23:35:00
2024-11-24T16:51:12.831433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:12.875647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct865
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
Minimum2024-01-01 00:00:00+00:00
Maximum2024-02-01 04:35:00+00:00
2024-11-24T16:51:12.919239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:12.969434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

runtime
Real number (ℝ)

High correlation  Missing 

Distinct108
Distinct (%)2.5%
Missing453
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean44.216428
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:13.085926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q118
median40
Q349
95-th percentile120
Maximum300
Range299
Interquartile range (IQR)31

Descriptive statistics

Standard deviation43.540048
Coefficient of variation (CV)0.98470296
Kurtosis11.833579
Mean44.216428
Median Absolute Deviation (MAD)17
Skewness3.0820534
Sum191634
Variance1895.7357
MonotonicityNot monotonic
2024-11-24T16:51:13.130789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 579
 
12.1%
15 314
 
6.6%
60 304
 
6.4%
30 205
 
4.3%
10 181
 
3.8%
120 142
 
3.0%
40 120
 
2.5%
43 120
 
2.5%
12 120
 
2.5%
3 116
 
2.4%
Other values (98) 2133
44.6%
(Missing) 453
 
9.5%
ValueCountFrequency (%)
1 7
 
0.1%
2 45
 
0.9%
3 116
2.4%
4 4
 
0.1%
5 41
 
0.9%
6 17
 
0.4%
7 39
 
0.8%
8 51
 
1.1%
9 17
 
0.4%
10 181
3.8%
ValueCountFrequency (%)
300 23
 
0.5%
240 71
1.5%
210 3
 
0.1%
205 1
 
< 0.1%
180 35
0.7%
173 1
 
< 0.1%
159 27
 
0.6%
150 3
 
0.1%
149 1
 
< 0.1%
142 2
 
< 0.1%

summary
Text

Missing 

Distinct1469
Distinct (%)99.6%
Missing3312
Missing (%)69.2%
Memory size37.5 KiB
2024-11-24T16:51:13.243788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2299
Median length455
Mean length208.66441
Min length27

Characters and Unicode

Total characters307780
Distinct characters163
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1463 ?
Unique (%)99.2%

Sample

1st row<p>Irina and Max are held captive by Anticuller. He finds out that Shusty has started digging under him to avoid robberies and decides to take revenge on him. Antikuller almost succeeds, but it turns out that all this time Shustry had his own plan of action, which even Kuvalda, Koshchey and Kilka did not know about....</p>
2nd row<p>Cal Quinn, leader of evangelical megachurch U Star, stuns his family by announcing an impulsive expansion into the US. But as family rivalries flare, Cal's relationship with troubled parishioner Rosa threatens to derail their ambitions.</p>
3rd row<p>Suffering a breakdown following Rosa's suicide, Cal locks horns with a psychologist who challenges his faith while the Quinns clash over who will lead Sunday Service in his absence. Jed meets a developer interested in the community centre.</p>
4th row<p>An erratic Cal returns to U Star with renewed plans to bring Jed back into the fold and solve the political threat of the Charity Tax bill with a Sleep Out fundraiser event. Abi draws Juno further into the church.</p>
5th row<p>A tragic accident leaves Moses in hospital and his family praying for a miracle. Issy confronts Benji about the secret he's been keeping while Maddox's latest scandal plunges Dion and U Star into hot water.</p>
ValueCountFrequency (%)
the 2676
 
5.3%
and 1710
 
3.4%
a 1697
 
3.4%
to 1665
 
3.3%
of 978
 
1.9%
in 805
 
1.6%
with 556
 
1.1%
is 552
 
1.1%
for 471
 
0.9%
his 451
 
0.9%
Other values (11319) 39006
77.1%
2024-11-24T16:51:13.419681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48939
15.9%
e 28143
 
9.1%
a 19950
 
6.5%
t 19469
 
6.3%
i 17063
 
5.5%
n 17017
 
5.5%
o 16482
 
5.4%
s 16271
 
5.3%
r 14640
 
4.8%
h 11730
 
3.8%
Other values (153) 98076
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 307780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
48939
15.9%
e 28143
 
9.1%
a 19950
 
6.5%
t 19469
 
6.3%
i 17063
 
5.5%
n 17017
 
5.5%
o 16482
 
5.4%
s 16271
 
5.3%
r 14640
 
4.8%
h 11730
 
3.8%
Other values (153) 98076
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 307780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
48939
15.9%
e 28143
 
9.1%
a 19950
 
6.5%
t 19469
 
6.3%
i 17063
 
5.5%
n 17017
 
5.5%
o 16482
 
5.4%
s 16271
 
5.3%
r 14640
 
4.8%
h 11730
 
3.8%
Other values (153) 98076
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 307780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
48939
15.9%
e 28143
 
9.1%
a 19950
 
6.5%
t 19469
 
6.3%
i 17063
 
5.5%
n 17017
 
5.5%
o 16482
 
5.4%
s 16271
 
5.3%
r 14640
 
4.8%
h 11730
 
3.8%
Other values (153) 98076
31.9%

rating.average
Real number (ℝ)

High correlation  Missing 

Distinct43
Distinct (%)12.7%
Missing4448
Missing (%)92.9%
Infinite0
Infinite (%)0.0%
Mean7.4952802
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:13.478665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5.68
Q16.8
median7.5
Q38.4
95-th percentile9.02
Maximum10
Range7
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.2127435
Coefficient of variation (CV)0.16180096
Kurtosis2.1206391
Mean7.4952802
Median Absolute Deviation (MAD)0.8
Skewness-0.9152831
Sum2540.9
Variance1.4707469
MonotonicityNot monotonic
2024-11-24T16:51:13.523884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
9 32
 
0.7%
7 27
 
0.6%
7.3 26
 
0.5%
7.5 20
 
0.4%
6.7 18
 
0.4%
8.5 18
 
0.4%
7.8 16
 
0.3%
6.5 15
 
0.3%
8 15
 
0.3%
6 12
 
0.3%
Other values (33) 140
 
2.9%
(Missing) 4448
92.9%
ValueCountFrequency (%)
3 3
 
0.1%
3.5 4
 
0.1%
4 4
 
0.1%
4.5 1
 
< 0.1%
5 1
 
< 0.1%
5.4 1
 
< 0.1%
5.5 3
 
0.1%
5.7 1
 
< 0.1%
6 12
0.3%
6.2 1
 
< 0.1%
ValueCountFrequency (%)
10 4
 
0.1%
9.7 1
 
< 0.1%
9.5 3
 
0.1%
9.4 3
 
0.1%
9.3 2
 
< 0.1%
9.2 4
 
0.1%
9 32
0.7%
8.9 1
 
< 0.1%
8.8 4
 
0.1%
8.7 11
 
0.2%

image.medium
Text

Missing 

Distinct1226
Distinct (%)100.0%
Missing3561
Missing (%)74.4%
Memory size37.5 KiB
2024-11-24T16:51:13.694256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length73
Median length73
Mean length73
Min length73

Characters and Unicode

Total characters89498
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1226 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_landscape/500/1251030.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/501/1254057.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/503/1257723.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/503/1257724.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/500/1251187.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/499/1249005.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/500/1251040.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/503/1257724.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/500/1251187.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/500/1251034.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/500/1251036.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/500/1251037.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/500/1251038.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/500/1251039.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/500/1251041.jpg 1
 
0.1%
Other values (1216) 1216
99.2%
2024-11-24T16:51:13.917134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 8582
 
9.6%
a 7356
 
8.2%
s 6130
 
6.8%
m 6130
 
6.8%
t 6130
 
6.8%
p 4904
 
5.5%
e 4904
 
5.5%
i 3678
 
4.1%
c 3678
 
4.1%
. 3678
 
4.1%
Other values (22) 34328
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 8582
 
9.6%
a 7356
 
8.2%
s 6130
 
6.8%
m 6130
 
6.8%
t 6130
 
6.8%
p 4904
 
5.5%
e 4904
 
5.5%
i 3678
 
4.1%
c 3678
 
4.1%
. 3678
 
4.1%
Other values (22) 34328
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 8582
 
9.6%
a 7356
 
8.2%
s 6130
 
6.8%
m 6130
 
6.8%
t 6130
 
6.8%
p 4904
 
5.5%
e 4904
 
5.5%
i 3678
 
4.1%
c 3678
 
4.1%
. 3678
 
4.1%
Other values (22) 34328
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 8582
 
9.6%
a 7356
 
8.2%
s 6130
 
6.8%
m 6130
 
6.8%
t 6130
 
6.8%
p 4904
 
5.5%
e 4904
 
5.5%
i 3678
 
4.1%
c 3678
 
4.1%
. 3678
 
4.1%
Other values (22) 34328
38.4%

image.original
Text

Missing 

Distinct1226
Distinct (%)100.0%
Missing3561
Missing (%)74.4%
Memory size37.5 KiB
2024-11-24T16:51:14.105901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length75
Median length75
Mean length75
Min length75

Characters and Unicode

Total characters91950
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1226 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/500/1251030.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/501/1254057.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/503/1257723.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/503/1257724.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/500/1251187.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/499/1249005.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/500/1251040.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/503/1257724.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/500/1251187.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/500/1251034.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/500/1251036.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/500/1251037.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/500/1251038.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/500/1251039.jpg 1
 
0.1%
https://static.tvmaze.com/uploads/images/original_untouched/500/1251041.jpg 1
 
0.1%
Other values (1216) 1216
99.2%
2024-11-24T16:51:14.328727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 8582
 
9.3%
t 7356
 
8.0%
a 6130
 
6.7%
s 4904
 
5.3%
i 4904
 
5.3%
o 4904
 
5.3%
p 3678
 
4.0%
c 3678
 
4.0%
. 3678
 
4.0%
g 3678
 
4.0%
Other values (23) 40458
44.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 8582
 
9.3%
t 7356
 
8.0%
a 6130
 
6.7%
s 4904
 
5.3%
i 4904
 
5.3%
o 4904
 
5.3%
p 3678
 
4.0%
c 3678
 
4.0%
. 3678
 
4.0%
g 3678
 
4.0%
Other values (23) 40458
44.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 8582
 
9.3%
t 7356
 
8.0%
a 6130
 
6.7%
s 4904
 
5.3%
i 4904
 
5.3%
o 4904
 
5.3%
p 3678
 
4.0%
c 3678
 
4.0%
. 3678
 
4.0%
g 3678
 
4.0%
Other values (23) 40458
44.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 8582
 
9.3%
t 7356
 
8.0%
a 6130
 
6.7%
s 4904
 
5.3%
i 4904
 
5.3%
o 4904
 
5.3%
p 3678
 
4.0%
c 3678
 
4.0%
. 3678
 
4.0%
g 3678
 
4.0%
Other values (23) 40458
44.0%

_links.self.href
Text

Unique 

Distinct4787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:14.468296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters186693
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4787 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2737127
2nd rowhttps://api.tvmaze.com/episodes/2688265
3rd rowhttps://api.tvmaze.com/episodes/2749976
4th rowhttps://api.tvmaze.com/episodes/2752362
5th rowhttps://api.tvmaze.com/episodes/2745754
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/2737127 1
 
< 0.1%
https://api.tvmaze.com/episodes/2749976 1
 
< 0.1%
https://api.tvmaze.com/episodes/2745754 1
 
< 0.1%
https://api.tvmaze.com/episodes/2745755 1
 
< 0.1%
https://api.tvmaze.com/episodes/2745756 1
 
< 0.1%
https://api.tvmaze.com/episodes/2745757 1
 
< 0.1%
https://api.tvmaze.com/episodes/2745758 1
 
< 0.1%
https://api.tvmaze.com/episodes/2745759 1
 
< 0.1%
https://api.tvmaze.com/episodes/2745760 1
 
< 0.1%
https://api.tvmaze.com/episodes/2745761 1
 
< 0.1%
Other values (4777) 4777
99.8%
2024-11-24T16:51:14.649165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 19148
 
10.3%
p 14361
 
7.7%
s 14361
 
7.7%
e 14361
 
7.7%
t 14361
 
7.7%
o 9574
 
5.1%
a 9574
 
5.1%
i 9574
 
5.1%
. 9574
 
5.1%
m 9574
 
5.1%
Other values (16) 62231
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 19148
 
10.3%
p 14361
 
7.7%
s 14361
 
7.7%
e 14361
 
7.7%
t 14361
 
7.7%
o 9574
 
5.1%
a 9574
 
5.1%
i 9574
 
5.1%
. 9574
 
5.1%
m 9574
 
5.1%
Other values (16) 62231
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 19148
 
10.3%
p 14361
 
7.7%
s 14361
 
7.7%
e 14361
 
7.7%
t 14361
 
7.7%
o 9574
 
5.1%
a 9574
 
5.1%
i 9574
 
5.1%
. 9574
 
5.1%
m 9574
 
5.1%
Other values (16) 62231
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 19148
 
10.3%
p 14361
 
7.7%
s 14361
 
7.7%
e 14361
 
7.7%
t 14361
 
7.7%
o 9574
 
5.1%
a 9574
 
5.1%
i 9574
 
5.1%
. 9574
 
5.1%
m 9574
 
5.1%
Other values (16) 62231
33.3%
Distinct690
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:14.802926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length34
Mean length33.935659
Min length32

Characters and Unicode

Total characters162450
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)1.8%

Sample

1st rowhttps://api.tvmaze.com/shows/59201
2nd rowhttps://api.tvmaze.com/shows/62222
3rd rowhttps://api.tvmaze.com/shows/63746
4th rowhttps://api.tvmaze.com/shows/69668
5th rowhttps://api.tvmaze.com/shows/70428
ValueCountFrequency (%)
https://api.tvmaze.com/shows/78854 100
 
2.1%
https://api.tvmaze.com/shows/73952 38
 
0.8%
https://api.tvmaze.com/shows/73703 36
 
0.8%
https://api.tvmaze.com/shows/73773 36
 
0.8%
https://api.tvmaze.com/shows/72654 36
 
0.8%
https://api.tvmaze.com/shows/74045 34
 
0.7%
https://api.tvmaze.com/shows/42056 33
 
0.7%
https://api.tvmaze.com/shows/69806 32
 
0.7%
https://api.tvmaze.com/shows/73931 30
 
0.6%
https://api.tvmaze.com/shows/73862 28
 
0.6%
Other values (680) 4384
91.6%
2024-11-24T16:51:15.001154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 19148
 
11.8%
s 14361
 
8.8%
t 14361
 
8.8%
h 9574
 
5.9%
p 9574
 
5.9%
a 9574
 
5.9%
o 9574
 
5.9%
. 9574
 
5.9%
m 9574
 
5.9%
e 4787
 
2.9%
Other values (16) 52349
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 19148
 
11.8%
s 14361
 
8.8%
t 14361
 
8.8%
h 9574
 
5.9%
p 9574
 
5.9%
a 9574
 
5.9%
o 9574
 
5.9%
. 9574
 
5.9%
m 9574
 
5.9%
e 4787
 
2.9%
Other values (16) 52349
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 19148
 
11.8%
s 14361
 
8.8%
t 14361
 
8.8%
h 9574
 
5.9%
p 9574
 
5.9%
a 9574
 
5.9%
o 9574
 
5.9%
. 9574
 
5.9%
m 9574
 
5.9%
e 4787
 
2.9%
Other values (16) 52349
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 19148
 
11.8%
s 14361
 
8.8%
t 14361
 
8.8%
h 9574
 
5.9%
p 9574
 
5.9%
a 9574
 
5.9%
o 9574
 
5.9%
. 9574
 
5.9%
m 9574
 
5.9%
e 4787
 
2.9%
Other values (16) 52349
32.2%
Distinct688
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:15.179865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length63
Median length41
Mean length17.535826
Min length2

Characters and Unicode

Total characters83944
Distinct characters168
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)1.8%

Sample

1st rowРесторан по понятиям
2nd rowСны Алисы
3rd rowСмотрите сами с ОК!
4th rowБольшое шоу
5th rowЗолотое дно
ValueCountFrequency (%)
the 777
 
5.2%
of 340
 
2.3%
my 226
 
1.5%
a 181
 
1.2%
love 179
 
1.2%
news 171
 
1.1%
and 170
 
1.1%
with 130
 
0.9%
you 122
 
0.8%
world 108
 
0.7%
Other values (1410) 12630
84.0%
2024-11-24T16:51:15.396664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10247
 
12.2%
e 7539
 
9.0%
a 5109
 
6.1%
o 4616
 
5.5%
i 4342
 
5.2%
n 4282
 
5.1%
r 3923
 
4.7%
t 3399
 
4.0%
s 3192
 
3.8%
l 2562
 
3.1%
Other values (158) 34733
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83944
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10247
 
12.2%
e 7539
 
9.0%
a 5109
 
6.1%
o 4616
 
5.5%
i 4342
 
5.2%
n 4282
 
5.1%
r 3923
 
4.7%
t 3399
 
4.0%
s 3192
 
3.8%
l 2562
 
3.1%
Other values (158) 34733
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83944
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10247
 
12.2%
e 7539
 
9.0%
a 5109
 
6.1%
o 4616
 
5.5%
i 4342
 
5.2%
n 4282
 
5.1%
r 3923
 
4.7%
t 3399
 
4.0%
s 3192
 
3.8%
l 2562
 
3.1%
Other values (158) 34733
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83944
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10247
 
12.2%
e 7539
 
9.0%
a 5109
 
6.1%
o 4616
 
5.5%
i 4342
 
5.2%
n 4282
 
5.1%
r 3923
 
4.7%
t 3399
 
4.0%
s 3192
 
3.8%
l 2562
 
3.1%
Other values (158) 34733
41.4%

_embedded.show.id
Real number (ℝ)

High correlation 

Distinct690
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63562.531
Minimum274
Maximum81004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:15.460420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum274
5-th percentile11502
Q159751
median72561
Q374045
95-th percentile77566.7
Maximum81004
Range80730
Interquartile range (IQR)14294

Descriptive statistics

Standard deviation18760.135
Coefficient of variation (CV)0.29514455
Kurtosis3.2160985
Mean63562.531
Median Absolute Deviation (MAD)3672
Skewness-1.9788023
Sum3.0427383 × 108
Variance3.5194266 × 108
MonotonicityNot monotonic
2024-11-24T16:51:15.510586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78854 100
 
2.1%
73952 38
 
0.8%
73703 36
 
0.8%
73773 36
 
0.8%
72654 36
 
0.8%
74045 34
 
0.7%
42056 33
 
0.7%
69806 32
 
0.7%
73931 30
 
0.6%
74100 28
 
0.6%
Other values (680) 4384
91.6%
ValueCountFrequency (%)
274 6
 
0.1%
703 4
 
0.1%
718 17
0.4%
729 4
 
0.1%
793 19
0.4%
802 5
 
0.1%
812 23
0.5%
875 3
 
0.1%
920 8
 
0.2%
938 6
 
0.1%
ValueCountFrequency (%)
81004 2
 
< 0.1%
80941 10
0.2%
80910 2
 
< 0.1%
80885 12
0.3%
80630 1
 
< 0.1%
80603 8
0.2%
80412 5
0.1%
80352 2
 
< 0.1%
80138 4
 
0.1%
80137 2
 
< 0.1%
Distinct690
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:15.678582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length97
Median length74
Mean length52.253395
Min length35

Characters and Unicode

Total characters250137
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)1.8%

Sample

1st rowhttps://www.tvmaze.com/shows/59201/restoran-po-ponatiam
2nd rowhttps://www.tvmaze.com/shows/62222/sny-alisy
3rd rowhttps://www.tvmaze.com/shows/63746/smotrite-sami-s-ok
4th rowhttps://www.tvmaze.com/shows/69668/bolsoe-sou
5th rowhttps://www.tvmaze.com/shows/70428/zolotoe-dno
ValueCountFrequency (%)
https://www.tvmaze.com/shows/78854/my-beautiful-dumb-wife 100
 
2.1%
https://www.tvmaze.com/shows/73952/shanghai-picked-flowers 38
 
0.8%
https://www.tvmaze.com/shows/73703/just-between-us 36
 
0.8%
https://www.tvmaze.com/shows/73773/my-boss 36
 
0.8%
https://www.tvmaze.com/shows/72654/our-interpreter 36
 
0.8%
https://www.tvmaze.com/shows/74045/sword-and-fairy-4 34
 
0.7%
https://www.tvmaze.com/shows/42056/like-a-flowing-river 33
 
0.7%
https://www.tvmaze.com/shows/69806/scout-hero 32
 
0.7%
https://www.tvmaze.com/shows/73931/different-princess 30
 
0.6%
https://www.tvmaze.com/shows/73862/born-to-run 28
 
0.6%
Other values (680) 4384
91.6%
2024-11-24T16:51:15.911664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 23935
 
9.6%
w 20649
 
8.3%
s 19140
 
7.7%
t 19125
 
7.6%
o 14912
 
6.0%
e 13017
 
5.2%
h 12536
 
5.0%
m 11737
 
4.7%
a 11185
 
4.5%
- 10203
 
4.1%
Other values (30) 93698
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 250137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 23935
 
9.6%
w 20649
 
8.3%
s 19140
 
7.7%
t 19125
 
7.6%
o 14912
 
6.0%
e 13017
 
5.2%
h 12536
 
5.0%
m 11737
 
4.7%
a 11185
 
4.5%
- 10203
 
4.1%
Other values (30) 93698
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 250137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 23935
 
9.6%
w 20649
 
8.3%
s 19140
 
7.7%
t 19125
 
7.6%
o 14912
 
6.0%
e 13017
 
5.2%
h 12536
 
5.0%
m 11737
 
4.7%
a 11185
 
4.5%
- 10203
 
4.1%
Other values (30) 93698
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 250137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 23935
 
9.6%
w 20649
 
8.3%
s 19140
 
7.7%
t 19125
 
7.6%
o 14912
 
6.0%
e 13017
 
5.2%
h 12536
 
5.0%
m 11737
 
4.7%
a 11185
 
4.5%
- 10203
 
4.1%
Other values (30) 93698
37.5%
Distinct688
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:16.071209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length63
Median length41
Mean length17.535826
Min length2

Characters and Unicode

Total characters83944
Distinct characters168
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)1.8%

Sample

1st rowРесторан по понятиям
2nd rowСны Алисы
3rd rowСмотрите сами с ОК!
4th rowБольшое шоу
5th rowЗолотое дно
ValueCountFrequency (%)
the 777
 
5.2%
of 340
 
2.3%
my 226
 
1.5%
a 181
 
1.2%
love 179
 
1.2%
news 171
 
1.1%
and 170
 
1.1%
with 130
 
0.9%
you 122
 
0.8%
world 108
 
0.7%
Other values (1410) 12630
84.0%
2024-11-24T16:51:16.286482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10247
 
12.2%
e 7539
 
9.0%
a 5109
 
6.1%
o 4616
 
5.5%
i 4342
 
5.2%
n 4282
 
5.1%
r 3923
 
4.7%
t 3399
 
4.0%
s 3192
 
3.8%
l 2562
 
3.1%
Other values (158) 34733
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83944
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10247
 
12.2%
e 7539
 
9.0%
a 5109
 
6.1%
o 4616
 
5.5%
i 4342
 
5.2%
n 4282
 
5.1%
r 3923
 
4.7%
t 3399
 
4.0%
s 3192
 
3.8%
l 2562
 
3.1%
Other values (158) 34733
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83944
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10247
 
12.2%
e 7539
 
9.0%
a 5109
 
6.1%
o 4616
 
5.5%
i 4342
 
5.2%
n 4282
 
5.1%
r 3923
 
4.7%
t 3399
 
4.0%
s 3192
 
3.8%
l 2562
 
3.1%
Other values (158) 34733
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83944
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10247
 
12.2%
e 7539
 
9.0%
a 5109
 
6.1%
o 4616
 
5.5%
i 4342
 
5.2%
n 4282
 
5.1%
r 3923
 
4.7%
t 3399
 
4.0%
s 3192
 
3.8%
l 2562
 
3.1%
Other values (158) 34733
41.4%

_embedded.show.type
Categorical

High correlation 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
Scripted
2241 
Animation
644 
News
534 
Reality
509 
Documentary
334 
Other values (6)
525 

Length

Max length11
Median length10
Mean length7.8475037
Min length4

Characters and Unicode

Total characters37566
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowScripted
2nd rowScripted
3rd rowTalk Show
4th rowGame Show
5th rowScripted

Common Values

ValueCountFrequency (%)
Scripted 2241
46.8%
Animation 644
 
13.5%
News 534
 
11.2%
Reality 509
 
10.6%
Documentary 334
 
7.0%
Talk Show 287
 
6.0%
Game Show 114
 
2.4%
Variety 56
 
1.2%
Sports 53
 
1.1%
Panel Show 14
 
0.3%

Length

2024-11-24T16:51:16.349344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
scripted 2241
43.1%
animation 644
 
12.4%
news 534
 
10.3%
reality 509
 
9.8%
show 416
 
8.0%
documentary 334
 
6.4%
talk 287
 
5.5%
game 114
 
2.2%
variety 56
 
1.1%
sports 53
 
1.0%
Other values (2) 15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 4094
10.9%
t 3837
 
10.2%
e 3802
 
10.1%
S 2710
 
7.2%
r 2685
 
7.1%
c 2575
 
6.9%
p 2294
 
6.1%
d 2242
 
6.0%
a 1959
 
5.2%
n 1636
 
4.4%
Other values (18) 9732
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4094
10.9%
t 3837
 
10.2%
e 3802
 
10.1%
S 2710
 
7.2%
r 2685
 
7.1%
c 2575
 
6.9%
p 2294
 
6.1%
d 2242
 
6.0%
a 1959
 
5.2%
n 1636
 
4.4%
Other values (18) 9732
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4094
10.9%
t 3837
 
10.2%
e 3802
 
10.1%
S 2710
 
7.2%
r 2685
 
7.1%
c 2575
 
6.9%
p 2294
 
6.1%
d 2242
 
6.0%
a 1959
 
5.2%
n 1636
 
4.4%
Other values (18) 9732
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4094
10.9%
t 3837
 
10.2%
e 3802
 
10.1%
S 2710
 
7.2%
r 2685
 
7.1%
c 2575
 
6.9%
p 2294
 
6.1%
d 2242
 
6.0%
a 1959
 
5.2%
n 1636
 
4.4%
Other values (18) 9732
25.9%

_embedded.show.language
Categorical

High correlation  Missing 

Distinct33
Distinct (%)0.7%
Missing330
Missing (%)6.9%
Memory size37.5 KiB
English
1649 
Chinese
1507 
Russian
247 
Norwegian
177 
Korean
 
106
Other values (28)
771 

Length

Max length10
Median length7
Mean length6.9930446
Min length4

Characters and Unicode

Total characters31168
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowRussian

Common Values

ValueCountFrequency (%)
English 1649
34.4%
Chinese 1507
31.5%
Russian 247
 
5.2%
Norwegian 177
 
3.7%
Korean 106
 
2.2%
Spanish 86
 
1.8%
Arabic 76
 
1.6%
Swedish 73
 
1.5%
Japanese 69
 
1.4%
Hindi 66
 
1.4%
Other values (23) 401
 
8.4%
(Missing) 330
 
6.9%

Length

2024-11-24T16:51:16.392826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 1649
37.0%
chinese 1507
33.8%
russian 247
 
5.5%
norwegian 177
 
4.0%
korean 106
 
2.4%
spanish 86
 
1.9%
arabic 76
 
1.7%
swedish 73
 
1.6%
japanese 69
 
1.5%
hindi 66
 
1.5%
Other values (23) 401
 
9.0%

Most occurring characters

ValueCountFrequency (%)
i 4246
13.6%
n 4198
13.5%
s 4039
13.0%
e 3637
11.7%
h 3551
11.4%
g 1864
6.0%
l 1728
5.5%
E 1649
 
5.3%
C 1517
 
4.9%
a 1158
 
3.7%
Other values (32) 3581
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4246
13.6%
n 4198
13.5%
s 4039
13.0%
e 3637
11.7%
h 3551
11.4%
g 1864
6.0%
l 1728
5.5%
E 1649
 
5.3%
C 1517
 
4.9%
a 1158
 
3.7%
Other values (32) 3581
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4246
13.6%
n 4198
13.5%
s 4039
13.0%
e 3637
11.7%
h 3551
11.4%
g 1864
6.0%
l 1728
5.5%
E 1649
 
5.3%
C 1517
 
4.9%
a 1158
 
3.7%
Other values (32) 3581
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4246
13.6%
n 4198
13.5%
s 4039
13.0%
e 3637
11.7%
h 3551
11.4%
g 1864
6.0%
l 1728
5.5%
E 1649
 
5.3%
C 1517
 
4.9%
a 1158
 
3.7%
Other values (32) 3581
11.5%

_embedded.show.genres
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size37.5 KiB

_embedded.show.status
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
Running
2454 
Ended
1721 
To Be Determined
612 

Length

Max length16
Median length7
Mean length7.4315855
Min length5

Characters and Unicode

Total characters35575
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowEnded
3rd rowRunning
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Running 2454
51.3%
Ended 1721
36.0%
To Be Determined 612
 
12.8%

Length

2024-11-24T16:51:16.432661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-24T16:51:16.464328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
running 2454
40.8%
ended 1721
28.6%
to 612
 
10.2%
be 612
 
10.2%
determined 612
 
10.2%

Most occurring characters

ValueCountFrequency (%)
n 9695
27.3%
e 4169
11.7%
d 4054
11.4%
i 3066
 
8.6%
R 2454
 
6.9%
u 2454
 
6.9%
g 2454
 
6.9%
E 1721
 
4.8%
1224
 
3.4%
T 612
 
1.7%
Other values (6) 3672
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 9695
27.3%
e 4169
11.7%
d 4054
11.4%
i 3066
 
8.6%
R 2454
 
6.9%
u 2454
 
6.9%
g 2454
 
6.9%
E 1721
 
4.8%
1224
 
3.4%
T 612
 
1.7%
Other values (6) 3672
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 9695
27.3%
e 4169
11.7%
d 4054
11.4%
i 3066
 
8.6%
R 2454
 
6.9%
u 2454
 
6.9%
g 2454
 
6.9%
E 1721
 
4.8%
1224
 
3.4%
T 612
 
1.7%
Other values (6) 3672
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 9695
27.3%
e 4169
11.7%
d 4054
11.4%
i 3066
 
8.6%
R 2454
 
6.9%
u 2454
 
6.9%
g 2454
 
6.9%
E 1721
 
4.8%
1224
 
3.4%
T 612
 
1.7%
Other values (6) 3672
 
10.3%

_embedded.show.runtime
Real number (ℝ)

High correlation  Missing 

Distinct48
Distinct (%)4.0%
Missing3582
Missing (%)74.8%
Infinite0
Infinite (%)0.0%
Mean60.4639
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:16.503135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q120
median45
Q360
95-th percentile240
Maximum300
Range299
Interquartile range (IQR)40

Descriptive statistics

Standard deviation62.028431
Coefficient of variation (CV)1.0258755
Kurtosis4.4780058
Mean60.4639
Median Absolute Deviation (MAD)21
Skewness2.1113454
Sum72859
Variance3847.5263
MonotonicityNot monotonic
2024-11-24T16:51:16.614917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
60 290
 
6.1%
120 106
 
2.2%
30 88
 
1.8%
10 71
 
1.5%
45 71
 
1.5%
12 48
 
1.0%
240 47
 
1.0%
20 44
 
0.9%
25 40
 
0.8%
11 29
 
0.6%
Other values (38) 371
 
7.8%
(Missing) 3582
74.8%
ValueCountFrequency (%)
1 6
 
0.1%
2 14
 
0.3%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 25
 
0.5%
6 2
 
< 0.1%
7 8
 
0.2%
8 24
 
0.5%
10 71
1.5%
11 29
0.6%
ValueCountFrequency (%)
300 23
 
0.5%
240 47
1.0%
210 3
 
0.1%
180 2
 
< 0.1%
159 27
 
0.6%
150 3
 
0.1%
120 106
2.2%
90 12
 
0.3%
75 11
 
0.2%
70 10
 
0.2%

_embedded.show.averageRuntime
Real number (ℝ)

High correlation  Missing 

Distinct99
Distinct (%)2.2%
Missing309
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean44.296784
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:16.660546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q118
median41
Q352
95-th percentile120
Maximum300
Range299
Interquartile range (IQR)34

Descriptive statistics

Standard deviation42.790837
Coefficient of variation (CV)0.96600324
Kurtosis12.2997
Mean44.296784
Median Absolute Deviation (MAD)17
Skewness3.1131133
Sum198361
Variance1831.0557
MonotonicityNot monotonic
2024-11-24T16:51:16.706897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 576
 
12.0%
60 344
 
7.2%
15 300
 
6.3%
30 247
 
5.2%
10 220
 
4.6%
43 148
 
3.1%
120 136
 
2.8%
3 117
 
2.4%
25 104
 
2.2%
40 97
 
2.0%
Other values (89) 2189
45.7%
(Missing) 309
 
6.5%
ValueCountFrequency (%)
1 6
 
0.1%
2 44
 
0.9%
3 117
2.4%
4 5
 
0.1%
5 33
 
0.7%
6 10
 
0.2%
7 52
 
1.1%
8 43
 
0.9%
9 19
 
0.4%
10 220
4.6%
ValueCountFrequency (%)
300 23
 
0.5%
242 2
 
< 0.1%
240 69
1.4%
218 1
 
< 0.1%
194 1
 
< 0.1%
184 1
 
< 0.1%
180 30
0.6%
177 4
 
0.1%
164 3
 
0.1%
163 27
 
0.6%
Distinct461
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
Minimum1944-01-20 00:00:00
Maximum2024-02-09 00:00:00
2024-11-24T16:51:16.751526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:16.799628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

_embedded.show.ended
Date

Missing 

Distinct76
Distinct (%)4.4%
Missing3066
Missing (%)64.0%
Memory size37.5 KiB
Minimum2024-01-01 00:00:00
Maximum2024-11-09 00:00:00
2024-11-24T16:51:16.846184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:16.895415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct610
Distinct (%)14.1%
Missing473
Missing (%)9.9%
Memory size37.5 KiB
2024-11-24T16:51:17.029841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length250
Median length104
Mean length52.155772
Min length16

Characters and Unicode

Total characters225000
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)1.7%

Sample

1st rowhttps://premier.one/show/restoran-po-ponyatiyam
2nd rowhttps://premier.one/show/sny-alisy
3rd rowhttps://premier.one/show/smotrite-sami-s-ok/season/1/
4th rowhttps://vk.com/azamatmusagaliev
5th rowhttps://premier.one/show/prelest
ValueCountFrequency (%)
https://flameserial.ru/season/12949 100
 
2.3%
https://abcnews.go.com/live 92
 
2.1%
https://v.qq.com/x/cover/mzc002005kvupzf.html 38
 
0.9%
https://v.youku.com/v_nextstage/id_ebdb60223f3e44c7aadf.html?spm=a2h0c.8166622.phonesokuprogram_1.dtitle 36
 
0.8%
https://w.mgtv.com/b/610526/20301892.html?fpa=se&lastp=so_result 36
 
0.8%
https://w.mgtv.com/h/600824/20020678.html 36
 
0.8%
https://www.iq.com/album/sword-and-fairy-4-2024-13ndvpx4xm1?lang=en_us 34
 
0.8%
https://v.qq.com/x/cover/mzc00200syv5tor.html 33
 
0.8%
https://www.iq.com/album/scout-hero-2023-1oipynj6bzh?lang=en_us 32
 
0.7%
https://v.youku.com/v_show/id_xnji5odc3mdm1mg==.html 30
 
0.7%
Other values (600) 3847
89.2%
2024-11-24T16:51:17.244344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 18490
 
8.2%
t 15672
 
7.0%
s 10828
 
4.8%
o 10527
 
4.7%
. 10252
 
4.6%
e 10019
 
4.5%
w 8887
 
3.9%
h 8561
 
3.8%
m 8458
 
3.8%
c 7965
 
3.5%
Other values (86) 115341
51.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 225000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 18490
 
8.2%
t 15672
 
7.0%
s 10828
 
4.8%
o 10527
 
4.7%
. 10252
 
4.6%
e 10019
 
4.5%
w 8887
 
3.9%
h 8561
 
3.8%
m 8458
 
3.8%
c 7965
 
3.5%
Other values (86) 115341
51.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 225000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 18490
 
8.2%
t 15672
 
7.0%
s 10828
 
4.8%
o 10527
 
4.7%
. 10252
 
4.6%
e 10019
 
4.5%
w 8887
 
3.9%
h 8561
 
3.8%
m 8458
 
3.8%
c 7965
 
3.5%
Other values (86) 115341
51.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 225000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 18490
 
8.2%
t 15672
 
7.0%
s 10828
 
4.8%
o 10527
 
4.7%
. 10252
 
4.6%
e 10019
 
4.5%
w 8887
 
3.9%
h 8561
 
3.8%
m 8458
 
3.8%
c 7965
 
3.5%
Other values (86) 115341
51.3%

_embedded.show.schedule.time
Categorical

High correlation  Imbalance 

Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2761 
12:00
440 
10:00
 
265
18:00
 
237
20:00
 
96
Other values (43)
988 

Length

Max length5
Median length0
Mean length2.1161479
Min length0

Characters and Unicode

Total characters10130
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row12:00
5th row

Common Values

ValueCountFrequency (%)
2761
57.7%
12:00 440
 
9.2%
10:00 265
 
5.5%
18:00 237
 
5.0%
20:00 96
 
2.0%
21:00 81
 
1.7%
13:00 78
 
1.6%
19:00 76
 
1.6%
06:00 74
 
1.5%
07:00 72
 
1.5%
Other values (38) 607
 
12.7%

Length

2024-11-24T16:51:17.308744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00 440
21.7%
10:00 265
13.1%
18:00 237
11.7%
20:00 96
 
4.7%
21:00 81
 
4.0%
13:00 78
 
3.8%
19:00 76
 
3.8%
06:00 74
 
3.7%
07:00 72
 
3.6%
09:00 64
 
3.2%
Other values (37) 543
26.8%

Most occurring characters

ValueCountFrequency (%)
0 4429
43.7%
: 2026
20.0%
1 1540
 
15.2%
2 878
 
8.7%
3 330
 
3.3%
8 247
 
2.4%
9 242
 
2.4%
7 157
 
1.5%
6 142
 
1.4%
5 109
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4429
43.7%
: 2026
20.0%
1 1540
 
15.2%
2 878
 
8.7%
3 330
 
3.3%
8 247
 
2.4%
9 242
 
2.4%
7 157
 
1.5%
6 142
 
1.4%
5 109
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4429
43.7%
: 2026
20.0%
1 1540
 
15.2%
2 878
 
8.7%
3 330
 
3.3%
8 247
 
2.4%
9 242
 
2.4%
7 157
 
1.5%
6 142
 
1.4%
5 109
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4429
43.7%
: 2026
20.0%
1 1540
 
15.2%
2 878
 
8.7%
3 330
 
3.3%
8 247
 
2.4%
9 242
 
2.4%
7 157
 
1.5%
6 142
 
1.4%
5 109
 
1.1%

_embedded.show.schedule.days
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size37.5 KiB

_embedded.show.rating.average
Real number (ℝ)

High correlation  Missing 

Distinct41
Distinct (%)5.5%
Missing4048
Missing (%)84.6%
Infinite0
Infinite (%)0.0%
Mean6.4428958
Minimum1
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:17.346564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q16.05
median6.8
Q37.3
95-th percentile7.9
Maximum8.2
Range7.2
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation1.3622824
Coefficient of variation (CV)0.21143946
Kurtosis4.0141512
Mean6.4428958
Median Absolute Deviation (MAD)0.6
Skewness-1.822675
Sum4761.3
Variance1.8558133
MonotonicityNot monotonic
2024-11-24T16:51:17.391790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
7 66
 
1.4%
7.1 43
 
0.9%
7.3 42
 
0.9%
7.4 42
 
0.9%
7.8 41
 
0.9%
6.7 34
 
0.7%
7.2 32
 
0.7%
6.8 30
 
0.6%
7.7 27
 
0.6%
6.3 27
 
0.6%
Other values (31) 355
 
7.4%
(Missing) 4048
84.6%
ValueCountFrequency (%)
1 7
 
0.1%
1.3 8
 
0.2%
2.1 10
0.2%
2.2 2
 
< 0.1%
4.1 6
 
0.1%
4.3 20
0.4%
4.4 19
0.4%
4.7 1
 
< 0.1%
4.8 24
0.5%
5 7
 
0.1%
ValueCountFrequency (%)
8.2 3
 
0.1%
8.1 4
 
0.1%
8 22
0.5%
7.9 11
 
0.2%
7.8 41
0.9%
7.7 27
0.6%
7.6 5
 
0.1%
7.5 12
 
0.3%
7.4 42
0.9%
7.3 42
0.9%

_embedded.show.weight
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.870065
Minimum0
Maximum100
Zeros134
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:17.439296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median19
Q349
95-th percentile94
Maximum100
Range100
Interquartile range (IQR)43

Descriptive statistics

Standard deviation30.061662
Coefficient of variation (CV)0.97381274
Kurtosis-0.48752094
Mean30.870065
Median Absolute Deviation (MAD)15
Skewness0.90708344
Sum147775
Variance903.70355
MonotonicityNot monotonic
2024-11-24T16:51:17.485894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 807
 
16.9%
8 310
 
6.5%
4 247
 
5.2%
12 166
 
3.5%
3 153
 
3.2%
23 136
 
2.8%
0 134
 
2.8%
26 122
 
2.5%
1 119
 
2.5%
18 102
 
2.1%
Other values (90) 2491
52.0%
ValueCountFrequency (%)
0 134
 
2.8%
1 119
 
2.5%
2 60
 
1.3%
3 153
 
3.2%
4 247
 
5.2%
5 28
 
0.6%
6 807
16.9%
7 72
 
1.5%
8 310
 
6.5%
9 13
 
0.3%
ValueCountFrequency (%)
100 3
 
0.1%
99 33
0.7%
98 36
0.8%
97 19
 
0.4%
96 70
1.5%
95 58
1.2%
94 41
0.9%
93 18
 
0.4%
92 30
0.6%
91 19
 
0.4%

_embedded.show.network
Unsupported

Missing  Rejected  Unsupported 

Missing4787
Missing (%)100.0%
Memory size37.5 KiB

_embedded.show.webChannel.id
Real number (ℝ)

High correlation  Missing 

Distinct147
Distinct (%)3.1%
Missing112
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean199.01925
Minimum1
Maximum643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:17.529412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q152
median104
Q3327
95-th percentile616
Maximum643
Range642
Interquartile range (IQR)275

Descriptive statistics

Standard deviation195.65914
Coefficient of variation (CV)0.98311666
Kurtosis-0.32533145
Mean199.01925
Median Absolute Deviation (MAD)83
Skewness0.99242705
Sum930415
Variance38282.5
MonotonicityNot monotonic
2024-11-24T16:51:17.576359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104 660
 
13.8%
21 355
 
7.4%
118 304
 
6.4%
26 299
 
6.2%
67 293
 
6.1%
1 247
 
5.2%
619 132
 
2.8%
86 127
 
2.7%
226 108
 
2.3%
3 99
 
2.1%
Other values (137) 2051
42.8%
(Missing) 112
 
2.3%
ValueCountFrequency (%)
1 247
5.2%
2 47
 
1.0%
3 99
 
2.1%
11 26
 
0.5%
12 4
 
0.1%
15 22
 
0.5%
20 12
 
0.3%
21 355
7.4%
26 299
6.2%
30 3
 
0.1%
ValueCountFrequency (%)
643 10
 
0.2%
632 8
 
0.2%
628 4
 
0.1%
623 45
 
0.9%
619 132
2.8%
616 92
1.9%
612 4
 
0.1%
609 6
 
0.1%
607 97
2.0%
600 8
 
0.2%
Distinct146
Distinct (%)3.1%
Missing112
Missing (%)2.3%
Memory size37.5 KiB
2024-11-24T16:51:17.720724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length8.2714439
Min length3

Characters and Unicode

Total characters38669
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowPremier
2nd rowPremier
3rd rowPremier
4th rowVK Видео
5th rowИви
ValueCountFrequency (%)
tencent 660
 
9.3%
qq 660
 
9.3%
youtube 355
 
5.0%
youku 304
 
4.3%
bbc 299
 
4.2%
iplayer 299
 
4.2%
tv 294
 
4.1%
iqiyi 293
 
4.1%
netflix 247
 
3.5%
news 189
 
2.7%
Other values (175) 3531
49.5%
2024-11-24T16:51:17.925883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3937
 
10.2%
2456
 
6.4%
n 2392
 
6.2%
i 2255
 
5.8%
o 1836
 
4.7%
a 1709
 
4.4%
t 1620
 
4.2%
T 1614
 
4.2%
Q 1613
 
4.2%
u 1587
 
4.1%
Other values (73) 17650
45.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3937
 
10.2%
2456
 
6.4%
n 2392
 
6.2%
i 2255
 
5.8%
o 1836
 
4.7%
a 1709
 
4.4%
t 1620
 
4.2%
T 1614
 
4.2%
Q 1613
 
4.2%
u 1587
 
4.1%
Other values (73) 17650
45.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3937
 
10.2%
2456
 
6.4%
n 2392
 
6.2%
i 2255
 
5.8%
o 1836
 
4.7%
a 1709
 
4.4%
t 1620
 
4.2%
T 1614
 
4.2%
Q 1613
 
4.2%
u 1587
 
4.1%
Other values (73) 17650
45.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3937
 
10.2%
2456
 
6.4%
n 2392
 
6.2%
i 2255
 
5.8%
o 1836
 
4.7%
a 1709
 
4.4%
t 1620
 
4.2%
T 1614
 
4.2%
Q 1613
 
4.2%
u 1587
 
4.1%
Other values (73) 17650
45.6%

_embedded.show.webChannel.country.name
Categorical

High correlation  Missing 

Distinct32
Distinct (%)1.0%
Missing1602
Missing (%)33.5%
Memory size37.5 KiB
China
1273 
United States
663 
United Kingdom
368 
Russian Federation
214 
Norway
140 
Other values (27)
527 

Length

Max length25
Median length18
Mean length9.1133438
Min length5

Characters and Unicode

Total characters29026
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRussian Federation
2nd rowRussian Federation
3rd rowRussian Federation
4th rowRussian Federation
5th rowRussian Federation

Common Values

ValueCountFrequency (%)
China 1273
26.6%
United States 663
13.9%
United Kingdom 368
 
7.7%
Russian Federation 214
 
4.5%
Norway 140
 
2.9%
India 72
 
1.5%
Canada 71
 
1.5%
Sweden 65
 
1.4%
Korea, Republic of 56
 
1.2%
Turkey 27
 
0.6%
Other values (22) 236
 
4.9%
(Missing) 1602
33.5%

Length

2024-11-24T16:51:17.993936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china 1282
28.0%
united 1031
22.5%
states 663
14.5%
kingdom 368
 
8.0%
russian 214
 
4.7%
federation 214
 
4.7%
norway 140
 
3.1%
india 72
 
1.6%
canada 71
 
1.6%
sweden 65
 
1.4%
Other values (28) 453
 
9.9%

Most occurring characters

ValueCountFrequency (%)
n 3475
12.0%
i 3391
11.7%
a 3083
10.6%
t 2657
 
9.2%
e 2504
 
8.6%
d 1856
 
6.4%
1388
 
4.8%
C 1353
 
4.7%
h 1311
 
4.5%
s 1137
 
3.9%
Other values (34) 6871
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 3475
12.0%
i 3391
11.7%
a 3083
10.6%
t 2657
 
9.2%
e 2504
 
8.6%
d 1856
 
6.4%
1388
 
4.8%
C 1353
 
4.7%
h 1311
 
4.5%
s 1137
 
3.9%
Other values (34) 6871
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 3475
12.0%
i 3391
11.7%
a 3083
10.6%
t 2657
 
9.2%
e 2504
 
8.6%
d 1856
 
6.4%
1388
 
4.8%
C 1353
 
4.7%
h 1311
 
4.5%
s 1137
 
3.9%
Other values (34) 6871
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 3475
12.0%
i 3391
11.7%
a 3083
10.6%
t 2657
 
9.2%
e 2504
 
8.6%
d 1856
 
6.4%
1388
 
4.8%
C 1353
 
4.7%
h 1311
 
4.5%
s 1137
 
3.9%
Other values (34) 6871
23.7%

_embedded.show.webChannel.country.code
Categorical

High correlation  Missing 

Distinct32
Distinct (%)1.0%
Missing1602
Missing (%)33.5%
Memory size37.5 KiB
CN
1273 
US
663 
GB
368 
RU
214 
NO
140 
Other values (27)
527 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6370
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRU
2nd rowRU
3rd rowRU
4th rowRU
5th rowRU

Common Values

ValueCountFrequency (%)
CN 1273
26.6%
US 663
13.9%
GB 368
 
7.7%
RU 214
 
4.5%
NO 140
 
2.9%
IN 72
 
1.5%
CA 71
 
1.5%
SE 65
 
1.4%
KR 56
 
1.2%
TR 27
 
0.6%
Other values (22) 236
 
4.9%
(Missing) 1602
33.5%

Length

2024-11-24T16:51:18.036287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cn 1273
40.0%
us 663
20.8%
gb 368
 
11.6%
ru 214
 
6.7%
no 140
 
4.4%
in 72
 
2.3%
ca 71
 
2.2%
se 65
 
2.0%
kr 56
 
1.8%
tr 27
 
0.8%
Other values (22) 236
 
7.4%

Most occurring characters

ValueCountFrequency (%)
N 1489
23.4%
C 1352
21.2%
U 918
14.4%
S 734
11.5%
G 395
 
6.2%
B 382
 
6.0%
R 313
 
4.9%
O 140
 
2.2%
E 135
 
2.1%
A 105
 
1.6%
Other values (13) 407
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1489
23.4%
C 1352
21.2%
U 918
14.4%
S 734
11.5%
G 395
 
6.2%
B 382
 
6.0%
R 313
 
4.9%
O 140
 
2.2%
E 135
 
2.1%
A 105
 
1.6%
Other values (13) 407
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1489
23.4%
C 1352
21.2%
U 918
14.4%
S 734
11.5%
G 395
 
6.2%
B 382
 
6.0%
R 313
 
4.9%
O 140
 
2.2%
E 135
 
2.1%
A 105
 
1.6%
Other values (13) 407
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1489
23.4%
C 1352
21.2%
U 918
14.4%
S 734
11.5%
G 395
 
6.2%
B 382
 
6.0%
R 313
 
4.9%
O 140
 
2.2%
E 135
 
2.1%
A 105
 
1.6%
Other values (13) 407
 
6.4%

_embedded.show.webChannel.country.timezone
Categorical

High correlation  Missing 

Distinct32
Distinct (%)1.0%
Missing1602
Missing (%)33.5%
Memory size37.5 KiB
Asia/Shanghai
1273 
America/New_York
663 
Europe/London
368 
Asia/Kamchatka
214 
Europe/Oslo
140 
Other values (27)
527 

Length

Max length19
Median length13
Mean length13.6854
Min length10

Characters and Unicode

Total characters43588
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAsia/Kamchatka
2nd rowAsia/Kamchatka
3rd rowAsia/Kamchatka
4th rowAsia/Kamchatka
5th rowAsia/Kamchatka

Common Values

ValueCountFrequency (%)
Asia/Shanghai 1273
26.6%
America/New_York 663
13.9%
Europe/London 368
 
7.7%
Asia/Kamchatka 214
 
4.5%
Europe/Oslo 140
 
2.9%
Asia/Kolkata 72
 
1.5%
America/Halifax 71
 
1.5%
Europe/Stockholm 65
 
1.4%
Asia/Seoul 56
 
1.2%
Europe/Istanbul 27
 
0.6%
Other values (22) 236
 
4.9%
(Missing) 1602
33.5%

Length

2024-11-24T16:51:18.074378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
asia/shanghai 1273
40.0%
america/new_york 663
20.8%
europe/london 368
 
11.6%
asia/kamchatka 214
 
6.7%
europe/oslo 140
 
4.4%
asia/kolkata 72
 
2.3%
america/halifax 71
 
2.2%
europe/stockholm 65
 
2.0%
asia/seoul 56
 
1.8%
europe/istanbul 27
 
0.8%
Other values (22) 236
 
7.4%

Most occurring characters

ValueCountFrequency (%)
a 6108
14.0%
i 3950
 
9.1%
/ 3185
 
7.3%
h 2840
 
6.5%
o 2618
 
6.0%
A 2468
 
5.7%
e 2331
 
5.3%
r 2285
 
5.2%
n 2173
 
5.0%
s 1979
 
4.5%
Other values (34) 13651
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6108
14.0%
i 3950
 
9.1%
/ 3185
 
7.3%
h 2840
 
6.5%
o 2618
 
6.0%
A 2468
 
5.7%
e 2331
 
5.3%
r 2285
 
5.2%
n 2173
 
5.0%
s 1979
 
4.5%
Other values (34) 13651
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6108
14.0%
i 3950
 
9.1%
/ 3185
 
7.3%
h 2840
 
6.5%
o 2618
 
6.0%
A 2468
 
5.7%
e 2331
 
5.3%
r 2285
 
5.2%
n 2173
 
5.0%
s 1979
 
4.5%
Other values (34) 13651
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6108
14.0%
i 3950
 
9.1%
/ 3185
 
7.3%
h 2840
 
6.5%
o 2618
 
6.0%
A 2468
 
5.7%
e 2331
 
5.3%
r 2285
 
5.2%
n 2173
 
5.0%
s 1979
 
4.5%
Other values (34) 13651
31.3%
Distinct88
Distinct (%)2.5%
Missing1334
Missing (%)27.9%
Memory size37.5 KiB
2024-11-24T16:51:18.182440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length250
Median length41
Mean length23.997683
Min length15

Characters and Unicode

Total characters82864
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowhttps://premier.one/
2nd rowhttps://premier.one/
3rd rowhttps://premier.one/
4th rowhttps://vk.com/video
5th rowhttps://www.ivi.ru/
ValueCountFrequency (%)
https://v.qq.com 660
19.1%
https://www.youtube.com 355
 
10.3%
https://www.bbc.co.uk/iplayer 299
 
8.7%
https://www.iq.com 293
 
8.5%
https://www.netflix.com 247
 
7.2%
https://edition.cnn.com/?hpt=header_edition-picker 132
 
3.8%
https://w.mgtv.com 108
 
3.1%
https://www.primevideo.com 99
 
2.9%
https://www.peacocktv.com 98
 
2.8%
https://abcnews.go.com/live 92
 
2.7%
Other values (78) 1070
31.0%
2024-11-24T16:51:18.354494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 9884
 
11.9%
t 8761
 
10.6%
. 7003
 
8.5%
w 6605
 
8.0%
p 4706
 
5.7%
o 4584
 
5.5%
c 4270
 
5.2%
s 4152
 
5.0%
h 3970
 
4.8%
: 3453
 
4.2%
Other values (38) 25476
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 9884
 
11.9%
t 8761
 
10.6%
. 7003
 
8.5%
w 6605
 
8.0%
p 4706
 
5.7%
o 4584
 
5.5%
c 4270
 
5.2%
s 4152
 
5.0%
h 3970
 
4.8%
: 3453
 
4.2%
Other values (38) 25476
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 9884
 
11.9%
t 8761
 
10.6%
. 7003
 
8.5%
w 6605
 
8.0%
p 4706
 
5.7%
o 4584
 
5.5%
c 4270
 
5.2%
s 4152
 
5.0%
h 3970
 
4.8%
: 3453
 
4.2%
Other values (38) 25476
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 9884
 
11.9%
t 8761
 
10.6%
. 7003
 
8.5%
w 6605
 
8.0%
p 4706
 
5.7%
o 4584
 
5.5%
c 4270
 
5.2%
s 4152
 
5.0%
h 3970
 
4.8%
: 3453
 
4.2%
Other values (38) 25476
30.7%

_embedded.show.dvdCountry
Unsupported

Missing  Rejected  Unsupported 

Missing4787
Missing (%)100.0%
Memory size37.5 KiB

_embedded.show.externals.tvrage
Real number (ℝ)

High correlation  Missing 

Distinct24
Distinct (%)13.5%
Missing4609
Missing (%)96.3%
Infinite0
Infinite (%)0.0%
Mean16543.444
Minimum712
Maximum47170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:18.413530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum712
5-th percentile1888
Q13256
median8531
Q332413
95-th percentile35853
Maximum47170
Range46458
Interquartile range (IQR)29157

Descriptive statistics

Standard deviation14527.157
Coefficient of variation (CV)0.87812173
Kurtosis-1.5450299
Mean16543.444
Median Absolute Deviation (MAD)6643
Skewness0.38256155
Sum2944733
Variance2.110383 × 108
MonotonicityNot monotonic
2024-11-24T16:51:18.450531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3256 23
 
0.5%
1888 20
 
0.4%
3418 19
 
0.4%
35853 17
 
0.4%
28327 11
 
0.2%
34149 10
 
0.2%
33858 10
 
0.2%
8531 8
 
0.2%
32413 6
 
0.1%
26056 6
 
0.1%
Other values (14) 48
 
1.0%
(Missing) 4609
96.3%
ValueCountFrequency (%)
712 2
 
< 0.1%
1888 20
0.4%
3005 4
 
0.1%
3256 23
0.5%
3418 19
0.4%
4920 4
 
0.1%
5152 4
 
0.1%
5199 6
 
0.1%
6659 5
 
0.1%
8531 8
 
0.2%
ValueCountFrequency (%)
47170 4
 
0.1%
35853 17
0.4%
34149 10
0.2%
33858 10
0.2%
32413 6
 
0.1%
31493 1
 
< 0.1%
30951 5
 
0.1%
28327 11
0.2%
27551 1
 
< 0.1%
26056 6
 
0.1%

_embedded.show.externals.thetvdb
Real number (ℝ)

High correlation  Missing 

Distinct493
Distinct (%)15.0%
Missing1504
Missing (%)31.4%
Infinite0
Infinite (%)0.0%
Mean395428.39
Minimum70366
Maximum449126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:18.491664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum70366
5-th percentile185615
Q1391619
median431091
Q3443249
95-th percentile444879
Maximum449126
Range378760
Interquartile range (IQR)51630

Descriptive statistics

Standard deviation85055.478
Coefficient of variation (CV)0.21509704
Kurtosis6.2665301
Mean395428.39
Median Absolute Deviation (MAD)13192
Skewness-2.5652281
Sum1.2981914 × 109
Variance7.2344344 × 109
MonotonicityNot monotonic
2024-11-24T16:51:18.609582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437967 36
 
0.8%
442007 36
 
0.8%
442306 34
 
0.7%
356549 33
 
0.7%
444283 30
 
0.6%
444879 28
 
0.6%
438826 28
 
0.6%
433681 26
 
0.5%
444128 26
 
0.5%
425012 24
 
0.5%
Other values (483) 2982
62.3%
(Missing) 1504
31.4%
ValueCountFrequency (%)
70366 23
0.5%
71178 2
 
< 0.1%
71753 19
0.4%
71756 4
 
0.1%
72716 4
 
0.1%
76355 6
 
0.1%
76719 19
0.4%
76779 5
 
0.1%
78006 20
0.4%
78419 4
 
0.1%
ValueCountFrequency (%)
449126 6
0.1%
448382 10
0.2%
447745 8
0.2%
447710 3
 
0.1%
447439 3
 
0.1%
447332 2
 
< 0.1%
447062 1
 
< 0.1%
446981 13
0.3%
446122 4
 
0.1%
446119 2
 
< 0.1%
Distinct347
Distinct (%)16.1%
Missing2635
Missing (%)55.0%
Memory size37.5 KiB
2024-11-24T16:51:18.735004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.7625465
Min length9

Characters and Unicode

Total characters21009
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)1.5%

Sample

1st rowtt22353310
2nd rowtt22353310
3rd rowtt22353310
4th rowtt22353310
5th rowtt22353310
ValueCountFrequency (%)
tt29367046 36
 
1.7%
tt9437032 33
 
1.5%
tt24060116 27
 
1.3%
tt21450424 23
 
1.1%
tt29894652 23
 
1.1%
tt0058796 23
 
1.1%
tt19382854 23
 
1.1%
tt15268270 23
 
1.1%
tt27654411 23
 
1.1%
tt14865358 22
 
1.0%
Other values (337) 1896
88.1%
2024-11-24T16:51:18.917386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 4304
20.5%
2 2304
11.0%
0 2069
9.8%
1 1872
8.9%
4 1826
8.7%
6 1638
 
7.8%
8 1609
 
7.7%
3 1591
 
7.6%
5 1312
 
6.2%
9 1284
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21009
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 4304
20.5%
2 2304
11.0%
0 2069
9.8%
1 1872
8.9%
4 1826
8.7%
6 1638
 
7.8%
8 1609
 
7.7%
3 1591
 
7.6%
5 1312
 
6.2%
9 1284
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21009
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 4304
20.5%
2 2304
11.0%
0 2069
9.8%
1 1872
8.9%
4 1826
8.7%
6 1638
 
7.8%
8 1609
 
7.7%
3 1591
 
7.6%
5 1312
 
6.2%
9 1284
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21009
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 4304
20.5%
2 2304
11.0%
0 2069
9.8%
1 1872
8.9%
4 1826
8.7%
6 1638
 
7.8%
8 1609
 
7.7%
3 1591
 
7.6%
5 1312
 
6.2%
9 1284
 
6.1%
Distinct658
Distinct (%)14.5%
Missing253
Missing (%)5.3%
Memory size37.5 KiB
2024-11-24T16:51:19.061400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length72
Median length72
Mean length71.840759
Min length68

Characters and Unicode

Total characters325726
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)1.8%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_portrait/391/978564.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/486/1217182.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/523/1307548.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/471/1179772.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/471/1179772.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/530/1326663.jpg 100
 
2.2%
https://static.tvmaze.com/uploads/images/medium_portrait/499/1249196.jpg 38
 
0.8%
https://static.tvmaze.com/uploads/images/medium_portrait/497/1243716.jpg 36
 
0.8%
https://static.tvmaze.com/uploads/images/medium_portrait/498/1246093.jpg 36
 
0.8%
https://static.tvmaze.com/uploads/images/medium_portrait/486/1216268.jpg 36
 
0.8%
https://static.tvmaze.com/uploads/images/medium_portrait/500/1250432.jpg 34
 
0.7%
https://static.tvmaze.com/uploads/images/medium_portrait/498/1247447.jpg 33
 
0.7%
https://static.tvmaze.com/uploads/images/medium_portrait/468/1170172.jpg 32
 
0.7%
https://static.tvmaze.com/uploads/images/medium_portrait/499/1248923.jpg 30
 
0.7%
https://static.tvmaze.com/uploads/images/medium_portrait/498/1247452.jpg 28
 
0.6%
Other values (648) 4131
91.1%
2024-11-24T16:51:19.233045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 31738
 
9.7%
t 31738
 
9.7%
a 22670
 
7.0%
m 22670
 
7.0%
p 18136
 
5.6%
s 18136
 
5.6%
i 18136
 
5.6%
. 13602
 
4.2%
e 13602
 
4.2%
o 13602
 
4.2%
Other values (22) 121696
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 325726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 31738
 
9.7%
t 31738
 
9.7%
a 22670
 
7.0%
m 22670
 
7.0%
p 18136
 
5.6%
s 18136
 
5.6%
i 18136
 
5.6%
. 13602
 
4.2%
e 13602
 
4.2%
o 13602
 
4.2%
Other values (22) 121696
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 325726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 31738
 
9.7%
t 31738
 
9.7%
a 22670
 
7.0%
m 22670
 
7.0%
p 18136
 
5.6%
s 18136
 
5.6%
i 18136
 
5.6%
. 13602
 
4.2%
e 13602
 
4.2%
o 13602
 
4.2%
Other values (22) 121696
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 325726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 31738
 
9.7%
t 31738
 
9.7%
a 22670
 
7.0%
m 22670
 
7.0%
p 18136
 
5.6%
s 18136
 
5.6%
i 18136
 
5.6%
. 13602
 
4.2%
e 13602
 
4.2%
o 13602
 
4.2%
Other values (22) 121696
37.4%
Distinct658
Distinct (%)14.5%
Missing253
Missing (%)5.3%
Memory size37.5 KiB
2024-11-24T16:51:19.363087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length75
Median length75
Mean length74.840759
Min length71

Characters and Unicode

Total characters339328
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)1.8%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/391/978564.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/486/1217182.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/523/1307548.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/471/1179772.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/471/1179772.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/530/1326663.jpg 100
 
2.2%
https://static.tvmaze.com/uploads/images/original_untouched/499/1249196.jpg 38
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/497/1243716.jpg 36
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/498/1246093.jpg 36
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/486/1216268.jpg 36
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/500/1250432.jpg 34
 
0.7%
https://static.tvmaze.com/uploads/images/original_untouched/498/1247447.jpg 33
 
0.7%
https://static.tvmaze.com/uploads/images/original_untouched/468/1170172.jpg 32
 
0.7%
https://static.tvmaze.com/uploads/images/original_untouched/499/1248923.jpg 30
 
0.7%
https://static.tvmaze.com/uploads/images/original_untouched/498/1247452.jpg 28
 
0.6%
Other values (648) 4131
91.1%
2024-11-24T16:51:19.538030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 31738
 
9.4%
t 27204
 
8.0%
a 22670
 
6.7%
s 18136
 
5.3%
i 18136
 
5.3%
o 18136
 
5.3%
p 13602
 
4.0%
c 13602
 
4.0%
. 13602
 
4.0%
g 13602
 
4.0%
Other values (23) 148900
43.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 339328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 31738
 
9.4%
t 27204
 
8.0%
a 22670
 
6.7%
s 18136
 
5.3%
i 18136
 
5.3%
o 18136
 
5.3%
p 13602
 
4.0%
c 13602
 
4.0%
. 13602
 
4.0%
g 13602
 
4.0%
Other values (23) 148900
43.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 339328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 31738
 
9.4%
t 27204
 
8.0%
a 22670
 
6.7%
s 18136
 
5.3%
i 18136
 
5.3%
o 18136
 
5.3%
p 13602
 
4.0%
c 13602
 
4.0%
. 13602
 
4.0%
g 13602
 
4.0%
Other values (23) 148900
43.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 339328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 31738
 
9.4%
t 27204
 
8.0%
a 22670
 
6.7%
s 18136
 
5.3%
i 18136
 
5.3%
o 18136
 
5.3%
p 13602
 
4.0%
c 13602
 
4.0%
. 13602
 
4.0%
g 13602
 
4.0%
Other values (23) 148900
43.9%
Distinct594
Distinct (%)14.9%
Missing795
Missing (%)16.6%
Memory size37.5 KiB
2024-11-24T16:51:19.686428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length1931
Median length637
Mean length383.70516
Min length50

Characters and Unicode

Total characters1531751
Distinct characters301
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)1.8%

Sample

1st row<p>Nimble is a first—class thief. After serving 6 years for unsuccessfully opening a safe, he decides to start leading a law-abiding lifestyle. He can't find a new job, and then a brilliant idea comes to his mind — to pull off the last robbery so that there are enough funds for a lifetime. To do this, he decides to rent a restaurant located next to a commercial bank and seek help from former cellmates.</p>
2nd row<p>A small northern town with a secret military facility nearby. There is an ordinary school, hospital and shop here, but any visitor will feel something strange. A girl Alice lives in this city. She has problems with her mother and at school, and she also has terrible dreams that predict the future. Alice dreams of running away from home with her only friend. But is it possible to get out of a city that has never let anyone go?</p>
3rd row<p>Weekly movie program: premiere reviews, exclusive interviews with top stars and reports from film sets, author ratings and exclusive selections.</p>
4th row<p>This is Azamat Musagaliyev's Big Show! Ten famous people are competing for a cash prize. The task is simple - DO NOT LAUGH! Who fails - out of the game. The last one, gets everything.</p>
5th row<p>A young car thief named Gypsy cannot forget his father's death. He wants to take revenge on the perpetrators, but does not know who they are, because 20 years have passed since the death. One day, Leonid Maksimovich, the former director of the local plant, appears in the city. This meeting becomes fateful for both.</p>
ValueCountFrequency (%)
the 15168
 
6.0%
and 9075
 
3.6%
to 7169
 
2.8%
of 7146
 
2.8%
a 7059
 
2.8%
in 4540
 
1.8%
is 2743
 
1.1%
with 2643
 
1.0%
her 2567
 
1.0%
his 2321
 
0.9%
Other values (8253) 191955
76.1%
2024-11-24T16:51:19.907494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
248035
16.2%
e 146470
 
9.6%
t 96530
 
6.3%
a 96398
 
6.3%
n 89530
 
5.8%
i 88781
 
5.8%
o 84941
 
5.5%
s 77267
 
5.0%
r 72811
 
4.8%
h 64792
 
4.2%
Other values (291) 466196
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1531751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
248035
16.2%
e 146470
 
9.6%
t 96530
 
6.3%
a 96398
 
6.3%
n 89530
 
5.8%
i 88781
 
5.8%
o 84941
 
5.5%
s 77267
 
5.0%
r 72811
 
4.8%
h 64792
 
4.2%
Other values (291) 466196
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1531751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
248035
16.2%
e 146470
 
9.6%
t 96530
 
6.3%
a 96398
 
6.3%
n 89530
 
5.8%
i 88781
 
5.8%
o 84941
 
5.5%
s 77267
 
5.0%
r 72811
 
4.8%
h 64792
 
4.2%
Other values (291) 466196
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1531751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
248035
16.2%
e 146470
 
9.6%
t 96530
 
6.3%
a 96398
 
6.3%
n 89530
 
5.8%
i 88781
 
5.8%
o 84941
 
5.5%
s 77267
 
5.0%
r 72811
 
4.8%
h 64792
 
4.2%
Other values (291) 466196
30.4%

_embedded.show.updated
Real number (ℝ)

High correlation 

Distinct690
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7166321 × 109
Minimum1.6983432 × 109
Maximum1.7324845 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:19.971679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.6983432 × 109
5-th percentile1.7048212 × 109
Q11.7067608 × 109
median1.7146294 × 109
Q31.7262471 × 109
95-th percentile1.7322608 × 109
Maximum1.7324845 × 109
Range34141341
Interquartile range (IQR)19486269

Descriptive statistics

Standard deviation10042374
Coefficient of variation (CV)0.0058500446
Kurtosis-1.4435763
Mean1.7166321 × 109
Median Absolute Deviation (MAD)8504148
Skewness0.29573949
Sum8.2175179 × 1012
Variance1.0084928 × 1014
MonotonicityNot monotonic
2024-11-24T16:51:20.018384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1723133542 100
 
2.1%
1707133992 38
 
0.8%
1705897985 36
 
0.8%
1706282249 36
 
0.8%
1706192291 36
 
0.8%
1706797142 34
 
0.7%
1711774278 33
 
0.7%
1706339205 32
 
0.7%
1706957455 30
 
0.6%
1706797129 28
 
0.6%
Other values (680) 4384
91.6%
ValueCountFrequency (%)
1698343176 4
0.1%
1699173762 4
0.1%
1699196321 3
0.1%
1700067953 1
 
< 0.1%
1701776723 7
0.1%
1703096478 4
0.1%
1703320852 7
0.1%
1703404987 3
0.1%
1703852377 4
0.1%
1703934794 4
0.1%
ValueCountFrequency (%)
1732484517 12
0.3%
1732468760 15
0.3%
1732459227 4
 
0.1%
1732458856 4
 
0.1%
1732457382 2
 
< 0.1%
1732444475 1
 
< 0.1%
1732444414 3
 
0.1%
1732443578 2
 
< 0.1%
1732443495 3
 
0.1%
1732443157 2
 
< 0.1%
Distinct690
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:20.155465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length34
Mean length33.935659
Min length32

Characters and Unicode

Total characters162450
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)1.8%

Sample

1st rowhttps://api.tvmaze.com/shows/59201
2nd rowhttps://api.tvmaze.com/shows/62222
3rd rowhttps://api.tvmaze.com/shows/63746
4th rowhttps://api.tvmaze.com/shows/69668
5th rowhttps://api.tvmaze.com/shows/70428
ValueCountFrequency (%)
https://api.tvmaze.com/shows/78854 100
 
2.1%
https://api.tvmaze.com/shows/73952 38
 
0.8%
https://api.tvmaze.com/shows/73703 36
 
0.8%
https://api.tvmaze.com/shows/73773 36
 
0.8%
https://api.tvmaze.com/shows/72654 36
 
0.8%
https://api.tvmaze.com/shows/74045 34
 
0.7%
https://api.tvmaze.com/shows/42056 33
 
0.7%
https://api.tvmaze.com/shows/69806 32
 
0.7%
https://api.tvmaze.com/shows/73931 30
 
0.6%
https://api.tvmaze.com/shows/73862 28
 
0.6%
Other values (680) 4384
91.6%
2024-11-24T16:51:20.354206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 19148
 
11.8%
s 14361
 
8.8%
t 14361
 
8.8%
h 9574
 
5.9%
p 9574
 
5.9%
a 9574
 
5.9%
o 9574
 
5.9%
. 9574
 
5.9%
m 9574
 
5.9%
e 4787
 
2.9%
Other values (16) 52349
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 19148
 
11.8%
s 14361
 
8.8%
t 14361
 
8.8%
h 9574
 
5.9%
p 9574
 
5.9%
a 9574
 
5.9%
o 9574
 
5.9%
. 9574
 
5.9%
m 9574
 
5.9%
e 4787
 
2.9%
Other values (16) 52349
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 19148
 
11.8%
s 14361
 
8.8%
t 14361
 
8.8%
h 9574
 
5.9%
p 9574
 
5.9%
a 9574
 
5.9%
o 9574
 
5.9%
. 9574
 
5.9%
m 9574
 
5.9%
e 4787
 
2.9%
Other values (16) 52349
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 19148
 
11.8%
s 14361
 
8.8%
t 14361
 
8.8%
h 9574
 
5.9%
p 9574
 
5.9%
a 9574
 
5.9%
o 9574
 
5.9%
. 9574
 
5.9%
m 9574
 
5.9%
e 4787
 
2.9%
Other values (16) 52349
32.2%
Distinct690
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:20.514128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters186693
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)1.8%

Sample

1st rowhttps://api.tvmaze.com/episodes/2737127
2nd rowhttps://api.tvmaze.com/episodes/2688265
3rd rowhttps://api.tvmaze.com/episodes/3053276
4th rowhttps://api.tvmaze.com/episodes/3054937
5th rowhttps://api.tvmaze.com/episodes/2745761
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/2975897 100
 
2.1%
https://api.tvmaze.com/episodes/2744151 38
 
0.8%
https://api.tvmaze.com/episodes/2726108 36
 
0.8%
https://api.tvmaze.com/episodes/2732738 36
 
0.8%
https://api.tvmaze.com/episodes/2740225 36
 
0.8%
https://api.tvmaze.com/episodes/2744350 34
 
0.7%
https://api.tvmaze.com/episodes/2755625 33
 
0.7%
https://api.tvmaze.com/episodes/2736579 32
 
0.7%
https://api.tvmaze.com/episodes/2739793 30
 
0.6%
https://api.tvmaze.com/episodes/2739269 28
 
0.6%
Other values (680) 4384
91.6%
2024-11-24T16:51:20.708109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 19148
 
10.3%
t 14361
 
7.7%
p 14361
 
7.7%
s 14361
 
7.7%
e 14361
 
7.7%
a 9574
 
5.1%
i 9574
 
5.1%
. 9574
 
5.1%
m 9574
 
5.1%
o 9574
 
5.1%
Other values (16) 62231
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 19148
 
10.3%
t 14361
 
7.7%
p 14361
 
7.7%
s 14361
 
7.7%
e 14361
 
7.7%
a 9574
 
5.1%
i 9574
 
5.1%
. 9574
 
5.1%
m 9574
 
5.1%
o 9574
 
5.1%
Other values (16) 62231
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 19148
 
10.3%
t 14361
 
7.7%
p 14361
 
7.7%
s 14361
 
7.7%
e 14361
 
7.7%
a 9574
 
5.1%
i 9574
 
5.1%
. 9574
 
5.1%
m 9574
 
5.1%
o 9574
 
5.1%
Other values (16) 62231
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 19148
 
10.3%
t 14361
 
7.7%
p 14361
 
7.7%
s 14361
 
7.7%
e 14361
 
7.7%
a 9574
 
5.1%
i 9574
 
5.1%
. 9574
 
5.1%
m 9574
 
5.1%
o 9574
 
5.1%
Other values (16) 62231
33.3%
Distinct523
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:20.880385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length92
Median length90
Mean length15.177773
Min length2

Characters and Unicode

Total characters72656
Distinct characters240
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)1.5%

Sample

1st rowСерия 34
2nd rowСерия 08
3rd rowСерия 144
4th rowБольшое шоу 10 сезон. Расширенная версия
5th rowСерия 8
ValueCountFrequency (%)
episode 2461
 
18.0%
the 393
 
2.9%
24 371
 
2.7%
36 193
 
1.4%
серия 180
 
1.3%
141
 
1.0%
8 136
 
1.0%
of 130
 
1.0%
and 125
 
0.9%
30 125
 
0.9%
Other values (1294) 9399
68.8%
2024-11-24T16:51:21.102171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8867
 
12.2%
e 5960
 
8.2%
o 4505
 
6.2%
i 4370
 
6.0%
s 4154
 
5.7%
d 3466
 
4.8%
p 2982
 
4.1%
E 2796
 
3.8%
a 2698
 
3.7%
n 2132
 
2.9%
Other values (230) 30726
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8867
 
12.2%
e 5960
 
8.2%
o 4505
 
6.2%
i 4370
 
6.0%
s 4154
 
5.7%
d 3466
 
4.8%
p 2982
 
4.1%
E 2796
 
3.8%
a 2698
 
3.7%
n 2132
 
2.9%
Other values (230) 30726
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8867
 
12.2%
e 5960
 
8.2%
o 4505
 
6.2%
i 4370
 
6.0%
s 4154
 
5.7%
d 3466
 
4.8%
p 2982
 
4.1%
E 2796
 
3.8%
a 2698
 
3.7%
n 2132
 
2.9%
Other values (230) 30726
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8867
 
12.2%
e 5960
 
8.2%
o 4505
 
6.2%
i 4370
 
6.0%
s 4154
 
5.7%
d 3466
 
4.8%
p 2982
 
4.1%
E 2796
 
3.8%
a 2698
 
3.7%
n 2132
 
2.9%
Other values (230) 30726
42.3%

image
Unsupported

Missing  Rejected  Unsupported 

Missing4787
Missing (%)100.0%
Memory size37.5 KiB

_embedded.show.image
Unsupported

Missing  Rejected  Unsupported 

Missing4787
Missing (%)100.0%
Memory size37.5 KiB
Distinct64
Distinct (%)13.3%
Missing4304
Missing (%)89.9%
Memory size37.5 KiB
2024-11-24T16:51:21.246349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters18837
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowhttps://api.tvmaze.com/episodes/3031619
2nd rowhttps://api.tvmaze.com/episodes/3047586
3rd rowhttps://api.tvmaze.com/episodes/3064190
4th rowhttps://api.tvmaze.com/episodes/3038211
5th rowhttps://api.tvmaze.com/episodes/3039212
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/3044190 23
 
4.8%
https://api.tvmaze.com/episodes/3063275 23
 
4.8%
https://api.tvmaze.com/episodes/3043813 22
 
4.6%
https://api.tvmaze.com/episodes/3062261 22
 
4.6%
https://api.tvmaze.com/episodes/3063279 22
 
4.6%
https://api.tvmaze.com/episodes/3038175 20
 
4.1%
https://api.tvmaze.com/episodes/3038211 19
 
3.9%
https://api.tvmaze.com/episodes/3047586 19
 
3.9%
https://api.tvmaze.com/episodes/3039212 18
 
3.7%
https://api.tvmaze.com/episodes/3059141 17
 
3.5%
Other values (54) 278
57.6%
2024-11-24T16:51:21.425312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 1932
 
10.3%
p 1449
 
7.7%
s 1449
 
7.7%
e 1449
 
7.7%
t 1449
 
7.7%
o 966
 
5.1%
a 966
 
5.1%
i 966
 
5.1%
. 966
 
5.1%
m 966
 
5.1%
Other values (16) 6279
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18837
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 1932
 
10.3%
p 1449
 
7.7%
s 1449
 
7.7%
e 1449
 
7.7%
t 1449
 
7.7%
o 966
 
5.1%
a 966
 
5.1%
i 966
 
5.1%
. 966
 
5.1%
m 966
 
5.1%
Other values (16) 6279
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18837
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 1932
 
10.3%
p 1449
 
7.7%
s 1449
 
7.7%
e 1449
 
7.7%
t 1449
 
7.7%
o 966
 
5.1%
a 966
 
5.1%
i 966
 
5.1%
. 966
 
5.1%
m 966
 
5.1%
Other values (16) 6279
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18837
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 1932
 
10.3%
p 1449
 
7.7%
s 1449
 
7.7%
e 1449
 
7.7%
t 1449
 
7.7%
o 966
 
5.1%
a 966
 
5.1%
i 966
 
5.1%
. 966
 
5.1%
m 966
 
5.1%
Other values (16) 6279
33.3%
Distinct52
Distinct (%)10.8%
Missing4304
Missing (%)89.9%
Memory size37.5 KiB
2024-11-24T16:51:21.555122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length88
Median length44
Mean length17.619048
Min length3

Characters and Unicode

Total characters8510
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowEpisode 135
2nd row25/11/2024
3rd rowРаж
4th rowEpisode 9152
5th row11/25/24
ValueCountFrequency (%)
episode 152
 
10.3%
matt 39
 
2.6%
day 31
 
2.1%
tba 31
 
2.1%
2024 27
 
1.8%
14995 23
 
1.6%
25 23
 
1.6%
ep 23
 
1.6%
221 23
 
1.6%
rogers 23
 
1.6%
Other values (123) 1077
73.2%
2024-11-24T16:51:21.730267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
989
 
11.6%
e 754
 
8.9%
o 531
 
6.2%
a 416
 
4.9%
s 386
 
4.5%
i 372
 
4.4%
n 310
 
3.6%
r 288
 
3.4%
2 280
 
3.3%
d 276
 
3.2%
Other values (67) 3908
45.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8510
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
989
 
11.6%
e 754
 
8.9%
o 531
 
6.2%
a 416
 
4.9%
s 386
 
4.5%
i 372
 
4.4%
n 310
 
3.6%
r 288
 
3.4%
2 280
 
3.3%
d 276
 
3.2%
Other values (67) 3908
45.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8510
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
989
 
11.6%
e 754
 
8.9%
o 531
 
6.2%
a 416
 
4.9%
s 386
 
4.5%
i 372
 
4.4%
n 310
 
3.6%
r 288
 
3.4%
2 280
 
3.3%
d 276
 
3.2%
Other values (67) 3908
45.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8510
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
989
 
11.6%
e 754
 
8.9%
o 531
 
6.2%
a 416
 
4.9%
s 386
 
4.5%
i 372
 
4.4%
n 310
 
3.6%
r 288
 
3.4%
2 280
 
3.3%
d 276
 
3.2%
Other values (67) 3908
45.9%

_embedded.show.network.id
Real number (ℝ)

High correlation  Missing 

Distinct40
Distinct (%)7.8%
Missing4271
Missing (%)89.2%
Infinite0
Infinite (%)0.0%
Mean570.05039
Minimum1
Maximum1963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.5 KiB
2024-11-24T16:51:21.793111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1166
median308
Q31039
95-th percentile1963
Maximum1963
Range1962
Interquartile range (IQR)873

Descriptive statistics

Standard deviation572.20746
Coefficient of variation (CV)1.003784
Kurtosis0.018955377
Mean570.05039
Median Absolute Deviation (MAD)232
Skewness1.0663366
Sum294146
Variance327421.38
MonotonicityNot monotonic
2024-11-24T16:51:21.838704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
481 45
 
0.9%
1282 41
 
0.9%
1 39
 
0.8%
276 36
 
0.8%
297 28
 
0.6%
1039 28
 
0.6%
1963 28
 
0.6%
166 24
 
0.5%
514 23
 
0.5%
3 22
 
0.5%
Other values (30) 202
 
4.2%
(Missing) 4271
89.2%
ValueCountFrequency (%)
1 39
0.8%
2 4
 
0.1%
3 22
0.5%
5 5
 
0.1%
29 10
 
0.2%
30 5
 
0.1%
40 4
 
0.1%
42 3
 
0.1%
52 3
 
0.1%
76 4
 
0.1%
ValueCountFrequency (%)
1963 28
0.6%
1766 4
 
0.1%
1683 15
 
0.3%
1501 1
 
< 0.1%
1328 9
 
0.2%
1282 41
0.9%
1058 15
 
0.3%
1039 28
0.6%
790 15
 
0.3%
758 4
 
0.1%

_embedded.show.network.name
Categorical

High correlation  Missing 

Distinct39
Distinct (%)7.6%
Missing4271
Missing (%)89.2%
Memory size37.5 KiB
Beijing TV
45 
CCTV-1
41 
NBC
39 
Hunan TV
36 
CCTV-8
 
28
Other values (34)
327 

Length

Max length21
Median length20
Mean length7.4282946
Min length3

Characters and Unicode

Total characters3833
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowТВ-3
2nd rowПятый канал
3rd rowMBC
4th rowBeijing TV
5th rowBeijing TV

Common Values

ValueCountFrequency (%)
Beijing TV 45
 
0.9%
CCTV-1 41
 
0.9%
NBC 39
 
0.8%
Hunan TV 36
 
0.8%
CCTV-8 28
 
0.6%
Disney Junior 28
 
0.6%
Shaanxi Satellite TV 28
 
0.6%
MBC 24
 
0.5%
ТВ-3 23
 
0.5%
ABC 23
 
0.5%
Other values (29) 201
 
4.2%
(Missing) 4271
89.2%

Length

2024-11-24T16:51:21.887449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tv 122
 
15.4%
beijing 45
 
5.7%
cctv-1 41
 
5.2%
nbc 39
 
4.9%
hunan 36
 
4.6%
cctv-8 28
 
3.5%
disney 28
 
3.5%
junior 28
 
3.5%
shaanxi 28
 
3.5%
satellite 28
 
3.5%
Other values (42) 367
46.5%

Most occurring characters

ValueCountFrequency (%)
C 280
 
7.3%
274
 
7.1%
n 262
 
6.8%
T 246
 
6.4%
i 224
 
5.8%
V 207
 
5.4%
e 204
 
5.3%
a 191
 
5.0%
B 151
 
3.9%
N 114
 
3.0%
Other values (53) 1680
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 280
 
7.3%
274
 
7.1%
n 262
 
6.8%
T 246
 
6.4%
i 224
 
5.8%
V 207
 
5.4%
e 204
 
5.3%
a 191
 
5.0%
B 151
 
3.9%
N 114
 
3.0%
Other values (53) 1680
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 280
 
7.3%
274
 
7.1%
n 262
 
6.8%
T 246
 
6.4%
i 224
 
5.8%
V 207
 
5.4%
e 204
 
5.3%
a 191
 
5.0%
B 151
 
3.9%
N 114
 
3.0%
Other values (53) 1680
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 280
 
7.3%
274
 
7.1%
n 262
 
6.8%
T 246
 
6.4%
i 224
 
5.8%
V 207
 
5.4%
e 204
 
5.3%
a 191
 
5.0%
B 151
 
3.9%
N 114
 
3.0%
Other values (53) 1680
43.8%

_embedded.show.network.country.name
Categorical

High correlation  Missing 

Distinct13
Distinct (%)2.5%
Missing4271
Missing (%)89.2%
Memory size37.5 KiB
China
178 
United States
160 
Russian Federation
57 
Korea, Republic of
43 
Denmark
21 
Other values (8)
57 

Length

Max length18
Median length14
Mean length10.352713
Min length5

Characters and Unicode

Total characters5342
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowRussian Federation
2nd rowRussian Federation
3rd rowKorea, Republic of
4th rowChina
5th rowChina

Common Values

ValueCountFrequency (%)
China 178
 
3.7%
United States 160
 
3.3%
Russian Federation 57
 
1.2%
Korea, Republic of 43
 
0.9%
Denmark 21
 
0.4%
Egypt 15
 
0.3%
Japan 12
 
0.3%
Hungary 11
 
0.2%
Czech Republic 9
 
0.2%
Saudi Arabia 4
 
0.1%
Other values (3) 6
 
0.1%
(Missing) 4271
89.2%

Length

2024-11-24T16:51:21.929908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china 178
21.4%
united 160
19.2%
states 160
19.2%
russian 57
 
6.9%
federation 57
 
6.9%
republic 52
 
6.2%
korea 43
 
5.2%
of 43
 
5.2%
denmark 21
 
2.5%
egypt 15
 
1.8%
Other values (8) 46
 
5.5%

Most occurring characters

ValueCountFrequency (%)
a 576
 
10.8%
e 561
 
10.5%
t 553
 
10.4%
i 513
 
9.6%
n 501
 
9.4%
316
 
5.9%
s 275
 
5.1%
d 224
 
4.2%
C 190
 
3.6%
h 187
 
3.5%
Other values (24) 1446
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 576
 
10.8%
e 561
 
10.5%
t 553
 
10.4%
i 513
 
9.6%
n 501
 
9.4%
316
 
5.9%
s 275
 
5.1%
d 224
 
4.2%
C 190
 
3.6%
h 187
 
3.5%
Other values (24) 1446
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 576
 
10.8%
e 561
 
10.5%
t 553
 
10.4%
i 513
 
9.6%
n 501
 
9.4%
316
 
5.9%
s 275
 
5.1%
d 224
 
4.2%
C 190
 
3.6%
h 187
 
3.5%
Other values (24) 1446
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 576
 
10.8%
e 561
 
10.5%
t 553
 
10.4%
i 513
 
9.6%
n 501
 
9.4%
316
 
5.9%
s 275
 
5.1%
d 224
 
4.2%
C 190
 
3.6%
h 187
 
3.5%
Other values (24) 1446
27.1%

_embedded.show.network.country.code
Categorical

High correlation  Missing 

Distinct13
Distinct (%)2.5%
Missing4271
Missing (%)89.2%
Memory size37.5 KiB
CN
178 
US
160 
RU
57 
KR
43 
DK
21 
Other values (8)
57 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1032
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowRU
2nd rowRU
3rd rowKR
4th rowCN
5th rowCN

Common Values

ValueCountFrequency (%)
CN 178
 
3.7%
US 160
 
3.3%
RU 57
 
1.2%
KR 43
 
0.9%
DK 21
 
0.4%
EG 15
 
0.3%
JP 12
 
0.3%
HU 11
 
0.2%
CZ 9
 
0.2%
SA 4
 
0.1%
Other values (3) 6
 
0.1%
(Missing) 4271
89.2%

Length

2024-11-24T16:51:21.968036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cn 178
34.5%
us 160
31.0%
ru 57
 
11.0%
kr 43
 
8.3%
dk 21
 
4.1%
eg 15
 
2.9%
jp 12
 
2.3%
hu 11
 
2.1%
cz 9
 
1.7%
sa 4
 
0.8%
Other values (3) 6
 
1.2%

Most occurring characters

ValueCountFrequency (%)
U 229
22.2%
C 190
18.4%
N 178
17.2%
S 164
15.9%
R 102
9.9%
K 64
 
6.2%
D 21
 
2.0%
E 15
 
1.5%
G 15
 
1.5%
J 12
 
1.2%
Other values (5) 42
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 229
22.2%
C 190
18.4%
N 178
17.2%
S 164
15.9%
R 102
9.9%
K 64
 
6.2%
D 21
 
2.0%
E 15
 
1.5%
G 15
 
1.5%
J 12
 
1.2%
Other values (5) 42
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 229
22.2%
C 190
18.4%
N 178
17.2%
S 164
15.9%
R 102
9.9%
K 64
 
6.2%
D 21
 
2.0%
E 15
 
1.5%
G 15
 
1.5%
J 12
 
1.2%
Other values (5) 42
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 229
22.2%
C 190
18.4%
N 178
17.2%
S 164
15.9%
R 102
9.9%
K 64
 
6.2%
D 21
 
2.0%
E 15
 
1.5%
G 15
 
1.5%
J 12
 
1.2%
Other values (5) 42
 
4.1%

_embedded.show.network.country.timezone
Categorical

High correlation  Missing 

Distinct13
Distinct (%)2.5%
Missing4271
Missing (%)89.2%
Memory size37.5 KiB
Asia/Shanghai
178 
America/New_York
160 
Asia/Kamchatka
57 
Asia/Seoul
43 
Europe/Copenhagen
21 
Other values (8)
57 

Length

Max length17
Median length16
Mean length13.895349
Min length10

Characters and Unicode

Total characters7170
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowAsia/Kamchatka
2nd rowAsia/Kamchatka
3rd rowAsia/Seoul
4th rowAsia/Shanghai
5th rowAsia/Shanghai

Common Values

ValueCountFrequency (%)
Asia/Shanghai 178
 
3.7%
America/New_York 160
 
3.3%
Asia/Kamchatka 57
 
1.2%
Asia/Seoul 43
 
0.9%
Europe/Copenhagen 21
 
0.4%
Africa/Cairo 15
 
0.3%
Asia/Tokyo 12
 
0.3%
Europe/Budapest 11
 
0.2%
Europe/Prague 9
 
0.2%
Asia/Riyadh 4
 
0.1%
Other values (3) 6
 
0.1%
(Missing) 4271
89.2%

Length

2024-11-24T16:51:22.009059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
asia/shanghai 178
34.5%
america/new_york 160
31.0%
asia/kamchatka 57
 
11.0%
asia/seoul 43
 
8.3%
europe/copenhagen 21
 
4.1%
africa/cairo 15
 
2.9%
asia/tokyo 12
 
2.3%
europe/budapest 11
 
2.1%
europe/prague 9
 
1.7%
asia/riyadh 4
 
0.8%
Other values (3) 6
 
1.2%

Most occurring characters

ValueCountFrequency (%)
a 1069
14.9%
i 675
 
9.4%
/ 516
 
7.2%
A 473
 
6.6%
e 472
 
6.6%
h 438
 
6.1%
r 408
 
5.7%
s 308
 
4.3%
o 306
 
4.3%
c 235
 
3.3%
Other values (25) 2270
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1069
14.9%
i 675
 
9.4%
/ 516
 
7.2%
A 473
 
6.6%
e 472
 
6.6%
h 438
 
6.1%
r 408
 
5.7%
s 308
 
4.3%
o 306
 
4.3%
c 235
 
3.3%
Other values (25) 2270
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1069
14.9%
i 675
 
9.4%
/ 516
 
7.2%
A 473
 
6.6%
e 472
 
6.6%
h 438
 
6.1%
r 408
 
5.7%
s 308
 
4.3%
o 306
 
4.3%
c 235
 
3.3%
Other values (25) 2270
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1069
14.9%
i 675
 
9.4%
/ 516
 
7.2%
A 473
 
6.6%
e 472
 
6.6%
h 438
 
6.1%
r 408
 
5.7%
s 308
 
4.3%
o 306
 
4.3%
c 235
 
3.3%
Other values (25) 2270
31.7%

_embedded.show.network.officialSite
Categorical

High correlation  Missing 

Distinct14
Distinct (%)8.9%
Missing4629
Missing (%)96.7%
Memory size37.5 KiB
https://www.nbc.com/
39 
https://tv3.ru/
23 
https://abc.com/
22 
https://www.foxnews.com/
22 
https://www.5-tv.ru/
15 
Other values (9)
37 

Length

Max length38
Median length32
Mean length20.025316
Min length15

Characters and Unicode

Total characters3164
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowhttps://tv3.ru/
2nd rowhttps://www.5-tv.ru/
3rd rowhttps://www.nbc.com/
4th rowhttps://abc.com/
5th rowhttps://tv.nova.cz/

Common Values

ValueCountFrequency (%)
https://www.nbc.com/ 39
 
0.8%
https://tv3.ru/ 23
 
0.5%
https://abc.com/ 22
 
0.5%
https://www.foxnews.com/ 22
 
0.5%
https://www.5-tv.ru/ 15
 
0.3%
https://tv.nova.cz/ 9
 
0.2%
https://www.usanetwork.com 5
 
0.1%
https://www.cwtv.com/ 5
 
0.1%
https://www.tbn.org/ 4
 
0.1%
https://www.cbs.com/ 4
 
0.1%
Other values (4) 10
 
0.2%
(Missing) 4629
96.7%

Length

2024-11-24T16:51:22.051978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.nbc.com 39
24.7%
https://tv3.ru 23
14.6%
https://abc.com 22
13.9%
https://www.foxnews.com 22
13.9%
https://www.5-tv.ru 15
 
9.5%
https://tv.nova.cz 9
 
5.7%
https://www.usanetwork.com 5
 
3.2%
https://www.cwtv.com 5
 
3.2%
https://www.tbn.org 4
 
2.5%
https://www.cbs.com 4
 
2.5%
Other values (4) 10
 
6.3%

Most occurring characters

ValueCountFrequency (%)
/ 472
14.9%
t 387
12.2%
w 335
10.6%
. 272
8.6%
s 192
 
6.1%
c 192
 
6.1%
p 164
 
5.2%
h 161
 
5.1%
: 158
 
5.0%
o 152
 
4.8%
Other values (19) 679
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 472
14.9%
t 387
12.2%
w 335
10.6%
. 272
8.6%
s 192
 
6.1%
c 192
 
6.1%
p 164
 
5.2%
h 161
 
5.1%
: 158
 
5.0%
o 152
 
4.8%
Other values (19) 679
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 472
14.9%
t 387
12.2%
w 335
10.6%
. 272
8.6%
s 192
 
6.1%
c 192
 
6.1%
p 164
 
5.2%
h 161
 
5.1%
: 158
 
5.0%
o 152
 
4.8%
Other values (19) 679
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 472
14.9%
t 387
12.2%
w 335
10.6%
. 272
8.6%
s 192
 
6.1%
c 192
 
6.1%
p 164
 
5.2%
h 161
 
5.1%
: 158
 
5.0%
o 152
 
4.8%
Other values (19) 679
21.5%

_embedded.show.webChannel
Unsupported

Missing  Rejected  Unsupported 

Missing4787
Missing (%)100.0%
Memory size37.5 KiB

_embedded.show.webChannel.country
Unsupported

Missing  Rejected  Unsupported 

Missing4787
Missing (%)100.0%
Memory size37.5 KiB
Distinct2
Distinct (%)50.0%
Missing4783
Missing (%)99.9%
Memory size37.5 KiB
2024-11-24T16:51:22.096862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length7
Mean length9.75
Min length7

Characters and Unicode

Total characters39
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st rowUkraine
2nd rowUkraine
3rd rowRussian Federation
4th rowUkraine
ValueCountFrequency (%)
ukraine 3
60.0%
russian 1
 
20.0%
federation 1
 
20.0%
2024-11-24T16:51:22.188797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5
12.8%
i 5
12.8%
n 5
12.8%
e 5
12.8%
r 4
10.3%
U 3
7.7%
k 3
7.7%
s 2
 
5.1%
R 1
 
2.6%
u 1
 
2.6%
Other values (5) 5
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5
12.8%
i 5
12.8%
n 5
12.8%
e 5
12.8%
r 4
10.3%
U 3
7.7%
k 3
7.7%
s 2
 
5.1%
R 1
 
2.6%
u 1
 
2.6%
Other values (5) 5
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5
12.8%
i 5
12.8%
n 5
12.8%
e 5
12.8%
r 4
10.3%
U 3
7.7%
k 3
7.7%
s 2
 
5.1%
R 1
 
2.6%
u 1
 
2.6%
Other values (5) 5
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5
12.8%
i 5
12.8%
n 5
12.8%
e 5
12.8%
r 4
10.3%
U 3
7.7%
k 3
7.7%
s 2
 
5.1%
R 1
 
2.6%
u 1
 
2.6%
Other values (5) 5
12.8%
Distinct2
Distinct (%)50.0%
Missing4783
Missing (%)99.9%
Memory size37.5 KiB
2024-11-24T16:51:22.221954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st rowUA
2nd rowUA
3rd rowRU
4th rowUA
ValueCountFrequency (%)
ua 3
75.0%
ru 1
 
25.0%
2024-11-24T16:51:22.290007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 4
50.0%
A 3
37.5%
R 1
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4
50.0%
A 3
37.5%
R 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4
50.0%
A 3
37.5%
R 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4
50.0%
A 3
37.5%
R 1
 
12.5%
Distinct2
Distinct (%)50.0%
Missing4783
Missing (%)99.9%
Memory size37.5 KiB
2024-11-24T16:51:22.348743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.75
Min length11

Characters and Unicode

Total characters47
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st rowEurope/Kyiv
2nd rowEurope/Kyiv
3rd rowAsia/Kamchatka
4th rowEurope/Kyiv
ValueCountFrequency (%)
europe/kyiv 3
75.0%
asia/kamchatka 1
 
25.0%
2024-11-24T16:51:22.449885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 4
 
8.5%
K 4
 
8.5%
a 4
 
8.5%
/ 4
 
8.5%
v 3
 
6.4%
u 3
 
6.4%
y 3
 
6.4%
E 3
 
6.4%
e 3
 
6.4%
p 3
 
6.4%
Other values (9) 13
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4
 
8.5%
K 4
 
8.5%
a 4
 
8.5%
/ 4
 
8.5%
v 3
 
6.4%
u 3
 
6.4%
y 3
 
6.4%
E 3
 
6.4%
e 3
 
6.4%
p 3
 
6.4%
Other values (9) 13
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4
 
8.5%
K 4
 
8.5%
a 4
 
8.5%
/ 4
 
8.5%
v 3
 
6.4%
u 3
 
6.4%
y 3
 
6.4%
E 3
 
6.4%
e 3
 
6.4%
p 3
 
6.4%
Other values (9) 13
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4
 
8.5%
K 4
 
8.5%
a 4
 
8.5%
/ 4
 
8.5%
v 3
 
6.4%
u 3
 
6.4%
y 3
 
6.4%
E 3
 
6.4%
e 3
 
6.4%
p 3
 
6.4%
Other values (9) 13
27.7%

Interactions

2024-11-24T16:51:10.276213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.679787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.204707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.737321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.301045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.811470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.384388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.928521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.433610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.030655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.566174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.079556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.671321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.126500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.670191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.309315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.719656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.240973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.769722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.336016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.846552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.420666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.961950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.468750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.064935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.601233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.114885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.701917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.162964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.707235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.345274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.756027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.277682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.866613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.371573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.880222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.459213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.999115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.506224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.102035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.637518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.152699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.734987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.201452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.744899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.377577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.788486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.311830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.897685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.404373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.914044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.493361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.031870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.539698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.139642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.669607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.186228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.764966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.237048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.780387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.411286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.822557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.346229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.930354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.437068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.943484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.529516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.065946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.574240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.172622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.703516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.221067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.796652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.271804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.815296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.439349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.860863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.380478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.963476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.469556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.976103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.563722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.094998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.608303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.203134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.734892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.253197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.826890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.307342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.849684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.476187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.897606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.418094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.998817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.506022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.008959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.601496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.133197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.647315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.240475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.772550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.290587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.859892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.346494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.887818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.509398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.931828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.453859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.032460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.540543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.040760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.638288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.166422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.684314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.270694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.806191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.326444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.888262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.382154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.924142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.543970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:02.966181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.489005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.065811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.574207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.074779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.674434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.200667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.718856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.307102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.841433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.361856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.920378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.417578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.959938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.576164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.002514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.526494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.102724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.609369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.104594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.711574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.231190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.823998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.363019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.875974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.465218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.952650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.455689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.995833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.609033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.035247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.560305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.134224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.641850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.158352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.746268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.264347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.857379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.396980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.908865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.499138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.983844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.491283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.031055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.643988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.070318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.596974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.167232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.676673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.190246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.783747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.299558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.892513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.430851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.942576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.533781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.013656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.528522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.066772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.671836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.098820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.626326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.196564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.704970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.219934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.813661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.328476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.921487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.464097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.971763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.561770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.040559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.556861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.166861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.708705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.135964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.664680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.231974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.740336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.323984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.853156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.364771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.958548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.501031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.008992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.599405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.069718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.594585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.204677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.743095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.171266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:03.700686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.267724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:04.776724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.355981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:05.890967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.400410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:06.994762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:07.536739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.044038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:08.635563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.099421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:09.633460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-24T16:51:10.240642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-24T16:51:22.566954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
_embedded.show.averageRuntime_embedded.show.externals.thetvdb_embedded.show.externals.tvrage_embedded.show.id_embedded.show.language_embedded.show.network.country.code_embedded.show.network.country.name_embedded.show.network.country.timezone_embedded.show.network.id_embedded.show.network.name_embedded.show.network.officialSite_embedded.show.rating.average_embedded.show.runtime_embedded.show.schedule.time_embedded.show.status_embedded.show.type_embedded.show.updated_embedded.show.webChannel.country.code_embedded.show.webChannel.country.name_embedded.show.webChannel.country.timezone_embedded.show.webChannel.id_embedded.show.weightairdateidnumberrating.averageruntimeseasontype
_embedded.show.averageRuntime1.000-0.2080.463-0.0360.2470.5670.5670.567-0.4400.7870.9500.0490.9910.4830.2050.2850.1000.2970.2970.2970.2580.0990.0660.002-0.1830.3390.9710.3810.000
_embedded.show.externals.thetvdb-0.2081.0000.8700.8570.3900.6000.6000.6000.1570.9070.857-0.174-0.2270.5220.3530.247-0.4190.3000.3000.3000.007-0.5800.0750.152-0.0040.002-0.164-0.7910.088
_embedded.show.externals.tvrage0.4630.8701.0000.0440.7270.7210.7210.721-0.7140.9861.000-0.2620.1230.4850.6540.597-0.1480.7580.7580.7580.221-0.0280.282-0.140-0.126-0.2090.531-0.3890.000
_embedded.show.id-0.0360.8570.0441.0000.2680.5200.5200.5200.1380.8410.903-0.1760.2800.4130.2880.214-0.1730.3190.3190.3190.241-0.7130.0920.4560.0690.157-0.009-0.3470.055
_embedded.show.language0.2470.3900.7270.2681.0000.9980.9980.9980.5390.9710.9700.3140.4940.3400.5610.3090.3020.9040.9040.9040.4950.2570.1100.2310.1060.2920.2660.4350.153
_embedded.show.network.country.code0.5670.6000.7210.5200.9981.0001.0001.0000.5360.9310.9700.8430.5480.7330.9260.4990.5600.9900.9900.9900.6210.5380.0000.2890.3250.5140.5220.5871.000
_embedded.show.network.country.name0.5670.6000.7210.5200.9981.0001.0001.0000.5360.9310.9700.8430.5480.7330.9260.4990.5600.9900.9900.9900.6210.5380.0000.2890.3250.5140.5220.5871.000
_embedded.show.network.country.timezone0.5670.6000.7210.5200.9981.0001.0001.0000.5360.9310.9700.8430.5480.7330.9260.4990.5600.9900.9900.9900.6210.5380.0000.2890.3250.5140.5220.5871.000
_embedded.show.network.id-0.4400.157-0.7140.1380.5390.5360.5360.5361.0000.9710.9700.534-0.4590.6820.4960.388-0.4240.6610.6610.661-0.137-0.4440.1190.1250.1030.573-0.406-0.2891.000
_embedded.show.network.name0.7870.9070.9860.8410.9710.9310.9310.9310.9711.0000.9970.9610.7590.8940.9570.8110.8890.9800.9800.9800.9630.8620.1040.8150.6620.3770.8060.9571.000
_embedded.show.network.officialSite0.9500.8571.0000.9030.9700.9700.9700.9700.9700.9971.0000.9640.8240.9260.9640.8230.8890.9690.9690.9690.9910.9230.0000.8120.6780.3820.9500.9501.000
_embedded.show.rating.average0.049-0.174-0.262-0.1760.3140.8430.8430.8430.5340.9610.9641.000-0.0310.3490.3120.3240.1310.3280.3280.328-0.0710.1730.342-0.2170.0240.3890.0620.1650.000
_embedded.show.runtime0.991-0.2270.1230.2800.4940.5480.5480.548-0.4590.7590.824-0.0311.0000.6170.2580.356-0.0190.4310.4310.4310.392-0.1550.0710.288-0.1040.4980.9860.5450.000
_embedded.show.schedule.time0.4830.5220.4850.4130.3400.7330.7330.7330.6820.8940.9260.3490.6171.0000.4090.3440.3250.4120.4120.4120.4250.3370.0860.2590.5060.2890.4250.5010.000
_embedded.show.status0.2050.3530.6540.2880.5610.9260.9260.9260.4960.9570.9640.3120.2580.4091.0000.5210.3890.6400.6400.6400.4380.2640.1630.1900.0900.3020.2180.4010.022
_embedded.show.type0.2850.2470.5970.2140.3090.4990.4990.4990.3880.8110.8230.3240.3560.3440.5211.0000.2140.4060.4060.4060.3160.1910.1110.4010.1010.1770.2970.8300.083
_embedded.show.updated0.100-0.419-0.148-0.1730.3020.5600.5600.560-0.4240.8890.8890.131-0.0190.3250.3890.2141.0000.3310.3310.3310.0780.2530.1700.2870.009-0.0640.0630.4960.047
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Missing values

2024-11-24T16:51:10.839258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-24T16:51:11.003370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-24T16:51:11.380744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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47792921730https://www.tvmaze.com/episodes/2921730/pbs-news-weekend-2024-01-21-episode-6Episode 620246.0regular2024-01-2117:002024-01-21T22:00:00+00:0030.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/2921730https://api.tvmaze.com/shows/10564PBS News Weekend10564https://www.tvmaze.com/shows/10564/pbs-news-weekendPBS News WeekendNewsEnglish[]Running30.030.02013-09-07Nonehttps://www.pbs.org/newshour/tag/newshour-weekend17:00[Saturday, Sunday]NaN32NaN420.0PBSUnited StatesUSAmerica/New_YorkNoneNaNNaN273048.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/488/1222172.jpghttps://static.tvmaze.com/uploads/images/original_untouched/488/1222172.jpg<p>Anchor John Yang presents a summary of the day's national and international news in a 30-minute program from the Tisch WNET Studios at Lincoln Center in New York. Each weekend broadcast contains original stories of national interest ranging from education and health care to the economy and finance. The <b>PBS News Weekend</b> team also works with local stations to produce stories that may appeal to a wider, national audience.</p>1718895747https://api.tvmaze.com/shows/10564https://api.tvmaze.com/episodes/2921778Episode 54NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
47802836672https://www.tvmaze.com/episodes/2836672/the-takeout-with-major-garrett-2024-01-21-the-takeout-rapper-fat-joeThe Takeout: Rapper Fat Joe20243.0regular2024-01-2117:002024-01-21T22:00:00+00:0060.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/2836672https://api.tvmaze.com/shows/76060The Takeout with Major Garrett76060https://www.tvmaze.com/shows/76060/the-takeout-with-major-garrettThe Takeout with Major GarrettNewsEnglish[]Running60.060.02024-01-07Nonehttps://www.cbsnews.com/the-takeout/17:00[Sunday]NaN4NaN607.0CBS NewsUnited StatesUSAmerica/New_YorkNoneNaNNaNNaNNoneNaNNaN<p>The Takeout is a weekly podcast about politics, policy and pop culture hosted by CBS News Chief White House Correspondent Major Garrett; analysis of the week's political news in a casual format that allows for expanded conversation with a newsmaker.</p>1720023625https://api.tvmaze.com/shows/76060https://api.tvmaze.com/episodes/2934983Episode 30NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
47812768872https://www.tvmaze.com/episodes/2768872/this-week-in-tech-2024-01-21-963-low-key-clippy-verizon-fees-ai-translations-microsoft-hack963: Low-Key Clippy - Verizon Fees, AI Translations, Microsoft Hack20243.0regular2024-01-2117:152024-01-21T22:15:00+00:00120.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/2768872https://api.tvmaze.com/shows/17584This Week in Tech17584https://www.tvmaze.com/shows/17584/this-week-in-techThis Week in TechNewsEnglish[]Running120.0120.02005-04-17Nonehttps://twit.tv/shows/this-week-in-tech17:15[Sunday]NaN30NaN102.0TwitUnited StatesUSAmerica/New_YorkNoneNaNNaN144991.0tt3541656https://static.tvmaze.com/uploads/images/medium_portrait/59/148015.jpghttps://static.tvmaze.com/uploads/images/original_untouched/59/148015.jpg<p>Your first podcast of the week is the last word in tech. Join the top tech pundits in a roundtable discussion of the latest trends in high tech. Hosted by Leo Laporte and friends.</p>1718882752https://api.tvmaze.com/shows/17584https://api.tvmaze.com/episodes/2918971984: Fifty-three Clicks - Bot Farms in Ukraine, LA Public Health Dept. PhishedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
47822725736https://www.tvmaze.com/episodes/2725736/sanningen-1x06-forsoningenFörsoningen16.0regular2024-01-2100:002024-01-21T23:00:00+00:0040.0<p>End of season. Iris can't let go of the Benjamin case and all of a sudden several pieces fall into place. Can the case be solved after all, after all these years? Kattis makes Iris realize the truth about Christian and the sisters reevaluate their relationship.</p>7.5https://static.tvmaze.com/uploads/images/medium_landscape/497/1243603.jpghttps://static.tvmaze.com/uploads/images/original_untouched/497/1243603.jpghttps://api.tvmaze.com/episodes/2725736https://api.tvmaze.com/shows/72738Sanningen72738https://www.tvmaze.com/shows/72738/sanningenSanningenScriptedSwedish[Drama, Crime]To Be DeterminedNaN40.02023-12-25Nonehttps://www.tv4play.se/program/c8c0d2ca6bd81bf5a291/sanningen00:00[]5.873NaN155.0TV4 PlaySwedenSEEurope/StockholmNoneNaNNaN443351.0tt19818812https://static.tvmaze.com/uploads/images/medium_portrait/496/1240359.jpghttps://static.tvmaze.com/uploads/images/original_untouched/496/1240359.jpg<p>Iris Broman is the new manager of the Kalla Fall group in Malmö. She has just broken up from her old life in Stockholm and moved into her half-sister's house by the sea in Ystad. On the same day that Iris starts her new job, a skull is found in the forest.</p>1730013576https://api.tvmaze.com/shows/72738https://api.tvmaze.com/episodes/2725736FörsoningenNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
47832681620https://www.tvmaze.com/episodes/2681620/vem-bor-har-9x08-vem-bor-i-kungens-festsalVem bor i kungens festsal?98.0regular2024-01-2102:002024-01-22T01:00:00+00:0060.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/2681620https://api.tvmaze.com/shows/22207Vem bor här22207https://www.tvmaze.com/shows/22207/vem-bor-harVem bor härGame ShowSwedish[]RunningNaN60.02021-04-12Nonehttps://www.svtplay.se/vem-bor-har02:00[Sunday]NaN13NaN190.0SVT PlaySwedenSEEurope/Stockholmhttps://www.svtplay.se/NaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/485/1213213.jpghttps://static.tvmaze.com/uploads/images/original_untouched/485/1213213.jpg<p>Who Lives Here?</p><p>Five strangers visiting each other's homes. The people know nothing about each other, except the name, profession and first impression. After the tour of the house are each to try to match the right person with the right home.</p>1699173762https://api.tvmaze.com/shows/22207https://api.tvmaze.com/episodes/2681621Vem har sommarängen inomhus?NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
47842737178https://www.tvmaze.com/episodes/2737178/min-sanning-18x02-elisabeth-ohlsonElisabeth Ohlson182.0regular2024-01-2102:002024-01-22T01:00:00+00:00NaNNoneNaNNaNNaNhttps://api.tvmaze.com/episodes/2737178https://api.tvmaze.com/shows/66359Min Sanning66359https://www.tvmaze.com/shows/66359/min-sanningMin SanningTalk ShowSwedish[]RunningNaN60.02013-01-29Nonehttps://www.svtplay.se/min-sanning02:00[]NaN2NaN190.0SVT PlaySwedenSEEurope/Stockholmhttps://www.svtplay.se/NaNNaN259345.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/441/1103786.jpghttps://static.tvmaze.com/uploads/images/original_untouched/441/1103786.jpg<p>In-depth conversations with famous Swedes. </p>1727329881https://api.tvmaze.com/shows/66359https://api.tvmaze.com/episodes/2737184Rissa SeidouNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
47852732771https://www.tvmaze.com/episodes/2732771/hej-och-tack-for-mig-1x03-dokumentarfilmarenDokumentärfilmaren13.0regular2024-01-2102:002024-01-22T01:00:00+00:0059.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/2732771https://api.tvmaze.com/shows/73775Hej och tack för mig73775https://www.tvmaze.com/shows/73775/hej-och-tack-for-migHej och tack för migDocumentarySwedish[]RunningNaN57.02024-01-07Nonehttps://www.svtplay.se/hej-och-tack-for-mig02:00[Sunday]NaN3NaN190.0SVT PlaySwedenSEEurope/Stockholmhttps://www.svtplay.se/NaNNaNNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/499/1248530.jpghttps://static.tvmaze.com/uploads/images/original_untouched/499/1248530.jpg<p>After more than 50 years in the service of television, Tom Alandh now looks back on his professional life and remembers and thinks about some of the people he met over the years. A journey through time and space through SVT's archives together with one of television's great storytellers.</p>1704950928https://api.tvmaze.com/shows/73775https://api.tvmaze.com/episodes/2732771DokumentärfilmarenNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
47862842173https://www.tvmaze.com/episodes/2842173/here-comes-the-sun-2024-01-21-episode-3Episode 320243.0regular2024-01-2104:302024-01-22T09:30:00+00:0030.0<p>Actor Taraji P. Henson sits down with Tracy Smith to discuss her latest film "The Color Purple" as well as other projects from her career. Then, Conor Knighton visits Alaska to celebrate 100 years of the Alaska Railroad. "Here Comes the Sun" is a closer look at some of the people, places and things we bring you every week on "CBS Sunday Morning." </p>NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/516/1291400.jpghttps://static.tvmaze.com/uploads/images/original_untouched/516/1291400.jpghttps://api.tvmaze.com/episodes/2842173https://api.tvmaze.com/shows/76062Here Comes the Sun76062https://www.tvmaze.com/shows/76062/here-comes-the-sunHere Comes the SunNewsEnglish[]Running30.030.02022-01-23Nonehttps://www.cbsnews.com/here-comes-the-sun/04:30[Sunday]NaN4NaN607.0CBS NewsUnited StatesUSAmerica/New_YorkNoneNaNNaNNaNNoneNaNNaN<p>Here Comes The Sun, anchored by Tracy Smith and Lee Cowan, takes a closer look at some of the people, places and things we bring you every weekend on Sunday Morning.</p>1720028273https://api.tvmaze.com/shows/76062https://api.tvmaze.com/episodes/2934991Episode 25NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN